Body-worn pulse oximeter

ABSTRACT

The invention provides a body-worn system that continuously measures pulse oximetry and blood pressure, along with motion, posture, and activity level, from an ambulatory patient. The system features an oximetry probe that comfortably clips to the base of the patient&#39;s thumb, thereby freeing up their fingers for conventional activities in a hospital, such as reading and eating. The probe secures to the thumb and measures time-dependent signals corresponding to LEDs operating near 660 and 905 nm. Analog versions of these signals pass through a low-profile cable to a wrist-worn transceiver that encloses a processing unit. Also within the wrist-worn transceiver is an accelerometer, a wireless system that sends information through a network to a remote receiver, e.g. a computer located in a central nursing station.

CROSS REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/218,055, filed Jun. 17, 2009, and to U.S. Provisional Application No.61/218,057, filed Jun. 17, 2009, and to U.S. Provisional Application No.61/218,059, filed Jun. 17, 2009, and to U.S. Provisional Application No.61/218,060, filed Jun. 17, 2009, and to U.S. Provisional Application No.61/218,061, filed Jun. 17, 2009, and to U.S. Provisional Application No.61/218,062, filed Jun. 17, 2009, all of which are incorporated herein byreference in their entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to medical devices for monitoring vitalsigns, e.g., saturation of peripheral oxygen, or SpO2.

2. Description of the Related Art

SpO2, sometimes referred to as the ‘fifth vital sign’, represents apatient's blood oxygen saturation. Medical professionals can detecthypoxemia, i.e. a deficiency of oxygen, by monitoring a patient's SpO2.Values between about 95-100% are considered normal; those below thisindicate hypoxemia, and will typically trigger an alarm in a hospitalsetting. A technique called pulse oximetry measures SpO2. Technicallythis parameter is determined from a patient's arterial oxygensaturation, or SaO2, which is a percentage of oxygenated arterialhemoglobin present in their blood. Functional hemoglobin molecules canbind with up to four oxygen molecules to yield ‘oxygenated’ hemoglobin(HbO2). A hemoglobin molecule bound to less than four oxygen moleculesis classified as ‘reduced’ hemoglobin (Hb). Conventional pulse oximetersfeature algorithms that assume only HbO2 and Hb are present in theblood, and measure SpO2 from the ratio of oxygenated hemoglobin to thetotal amount of hemoglobin (both oxygenated and reduced) according toequation (1):

$\begin{matrix}{{{SpO}\; 2} = \frac{{HbO}\; 2}{{{HbO}\; 2} + {Hb}}} & (1)\end{matrix}$

HbO2 and Hb feature different absorption spectra in the visible andinfrared regions, and can therefore be measured optically. Conventionalpulse oximeters thus typically feature light sources (most typicallylight-emitting diodes, or LEDs) that radiate in the red (near 660 nm)and infrared (typically between 900-950 nm) spectral regions. Aphotodetector measures a portion of radiation at each wavelength thattransmits through the patient's pulsating blood, but is not absorbed. At660 nm, for example, Hb absorbs about ten times as much radiation asHbO2, whereas at 905 nm HbO2 absorbs about two times as much radiationas Hb. Detection of transmitted radiation at these wavelengths yieldstwo time-dependent waveforms, each called a plethysmogram (PPG), that anoximeter analyzes to solve for SpO2 as defined in equation (1) above.

Specifically, the oximeter processes PPG waveforms measured with red(RED(PPG)) and infrared (IR(PPG)) wavelengths to determinetime-dependent AC signals and time-independent DC signals. The term ‘AC’signals, as used herein, refers to a portion of a PPG waveform thatvaries relatively rapidly with time, e.g. the portion of the signaloriginating by pulsations in the patient's blood. ‘DC’ signals, incontrast, are portions of the PPG that are relatively invariant withtime, e.g. the portion of the signal originating from scattering off ofcomponents such as bone, skin, and non-pulsating components of thepatient's blood.

More specifically, AC signals are measured from a heartbeat-inducedpulse present in both waveforms. The pulse represents a pressure wave,launched by the heart, which propagates through the patient'svasculature and causes a time-dependent increase in volume in botharteries and capillaries. When the pressure pulse reaches vasculatureirradiated by the oximeter's optical system, a temporary volumetricincrease results in a relatively large optical absorption according tothe Beer-Lambert Law. DC signals originate from radiation scatteringfrom static components such as bone, skin, and relatively non-pulsatilecomponents of both arterial and venous blood. Typically only about0.5-1% of the total signal measured by the photodetector originates fromthe AC signal, with the remainder originating from the DC signal.Separation of AC and DC signals is typically done with both analog anddigital filtering techniques that are well-known in the art.

During pulse oximetry a normalized ‘r’ value is typically calculatedfrom AC and DC signals using equation (2), below:

$\begin{matrix}{r = \frac{660\mspace{11mu} {{{nm}({AC})}/660}\mspace{11mu} {{nm}({DC})}}{905\mspace{11mu} {{{nm}({AC})}/905}\mspace{11mu} {{nm}({DC})}}} & (2)\end{matrix}$

r, which is sometimes called a ‘ratio of ratios’ (RoR), represents aratio of Hb to HbO2. It equates an actual SpO2 value, which ranges from0-100% O2, to an empirical relationship that resembles a non-linearequation. Above about 70% O2 this equation typically yields values thatare accurate to a few percent. Measurements below this value, while notnecessarily accurate, still indicate a hypoxic patient in need ofmedical attention.

Pulse oximeters for measuring SpO2 were originally developed in 1972,and have evolved over the last 30 years to a point where they arecommonplace in nearly all vital sign monitors for in-hospital use.Typical pulse oximeters feature a probe encased in a clothespin-shapedhousing that includes both red and infrared LEDs, and a photodetectorthat detects radiation from the LEDs after it passes through a portionof the patient's body. The probe typically clips to a patient's indexfinger. Most probes operate in a transmission-mode optical geometry, andrelay analog waveforms measured by LEDs and the photodetector to anexternal processing unit. The processing unit is typically integratedinto a stand-alone monitor that measures only SpO2 and pulse rate(determined from the AC signal of one of the PPG waveforms), or acomplete vital sign monitor that measures SpO2 along with systolic(SYS), mean (MAP), and diastolic (DIA) blood pressure, heart rate (HR),respiratory rate (RR), and temperature (TEMP). In both cases theoximeter probe typically connects to the monitor through a cable.Alternate configurations of SpO2 monitors include those that operate inreflection-mode optical geometries, probes that clip onto appendagesother than the patient's finger (e.g. their ear or forehead), andprocessing units that are worn directly on the patient's body (e.g.their wrist). In some cases PPG waveforms, along with SpO2 and pulserate values, are sent wirelessly from the oximeter to a remote display.

Because it is based on an optical measurement, pulse oximetry can beextremely sensitive to a patient's motion. Activities such as walking,finger tapping, falling, and convulsing can result in a number ofartifacts that distort both the AC and DC components of waveformsmeasured with the oximeter's optical system. Motion-related activities,for example, can cause the oximeter probe to move relative to thepatient's finger, change the amount of ambient light that irradiates thephotodetector, and disrupt both arterial and venus blood flow invasculature measured by the optical system. Each of these events cangenerate artifacts that, in some cases, are similar to the AC and DCsignals within the PPG waveforms. Ultimately this can cause the pulseoximeter to generate inaccurate values and false alarms.

Oximeters suffer other problems outside of their measurement accuracy.Probes encapsulating a patient's index finger can be uncomfortable andawkward, especially when worn for extended periods of time. Pulseoximeters that lack body-worn processing units can only providemeasurements when a patient is sedentary and attached to a bedsidemonitor; they are impractical for ambulatory patients moving about thehospital, making it difficult to provide true continuous monitoring.Most body-worn oximeters typically lack systems for continuouslymeasuring all vital signs, and particularly blood pressure, from apatient.

SUMMARY OF THE INVENTION

The invention described herein provides a body-worn monitor thatcontinuously measures pulse oximetry and other vital signs, along withmotion, posture, and activity level, from an ambulatory patient. Thesystem features an oximetry probe that comfortably clips to the base ofthe patient's thumb, thereby freeing up their fingers for conventionalactivities in a hospital, such as reading and eating. The probereversibly secures to the thumb with, e.g., an easy-to-use Velcro strap,disposable tape, or similar closure, or may be provided in the form of aclosed ring which slips over the thumb. It measures time-dependentwaveforms (RED/IR(PPG)) corresponding to LEDs typically operating near660 nm and 905 nm. Analog versions of these waveforms pass through alow-profile cable to a wrist-worn transceiver enclosing a processingunit. Also within the wrist-worn transceiver is a wireless system(typically based wireless protocols such as 802.11 and 802.15.4) thatsends information through a network to a remote receiver, e.g. acomputer located in a central nursing station.

Clinically accurate pulse oximetry measurements made at the base of thepatient's thumb require a set of coefficients relating r (from Eq. 2) toSpO2 that are typically determined with a set of empirical experiments(e.g. a ‘breathe down’ study, described below). These coefficientsdiffer from those used in conventional oximetry measurements because ofthe differences between vasculature in the base of the thumb and the tipof the index finger. Typically the base of the thumb features relativelyfewer capillary beds, and thus the coefficients are preferably adjustedaccordingly.

Three motion-detecting sensors (e.g. accelerometers) form part of thebody-worn monitoring system. They are typically secured to the patient'storso (e.g. chest), upper arm (e.g. bicep), and lower arm (e.g. wrist),and measure time-dependent motion signals (ACC waveforms). Thewrist-worn transceiver receives and processes these motion signals todetermine the patient's degree of motion, posture, and activity level.Each sensor typically measures a unique ACC waveform along three axes(x, y, and z), and ultimately yields information that can be processedto determine a separate component of the patient's motion. For example,the sensor worn on the lower arm (which may be within the wrist-worntransceiver) monitors movement of the patient's hand and fingers; suchmotion typically disrupts the RED/IR(PPG) waveforms. It can therefore beprocessed and used to exclude certain noise-corrupted artifacts from theSpO2 calculation. Sensors attached to the lower and upper arms eachmeasure signals that are collectively analyzed to estimate the patient'sarm height; this can be used to improve accuracy of a continuous bloodpressure measurement (cNIBP), as described below. And the sensorattached to the patient's chest measures signals that are analyzed todetermine the patient's posture and activity level, which can affectmeasurements for SpO2, cNIBP, and other vital signs. Algorithms forprocessing information from the accelerometers for these purposes aredescribed in detail in the following patent applications, the contentsof which are fully incorporated herein by reference: BODY-WORN MONITORFEATURING ALARM SYSTEM THAT PROCESSES A PATIENT'S MOTION AND VITAL SIGNS(U.S. Ser. No. 12/469,182; filed May 20, 2009) and BODY-WORN VITAL SIGNMONITOR WITH SYSTEM FOR DETECTING AND ANALYZING MOTION (U.S. Ser. No,12/469,094; filed May 20, 2009). As described therein, knowledge of apatient's motion, activity level, and posture can greatly enhance theaccuracy of alarms/alerts generated by the body-worn monitor. Forexample, a walking patient typically yields noisy PPG waveforms, butalso has a low probability of being hypoxic due to their activity state.According to the invention, a patient in this condition thus does nottypically generate an alarm/alert, regardless of the value of SpO2 thatis measured. Similarly, a patient that is convulsing or fallingtypically yields noisy RED/IR(PPG) waveforms from which it is difficultto extract an SpO2 value. But these activity states, regardless of thepatient's SpO2 values, may trigger an alarm/alert because they indicatethe patient needs medical assistance.

The body-worn monitor features systems for continuously monitoringpatients in a hospital environment, and as the patient ultimatelytransfers from the hospital to the home. Both SpO2 and cNIBP rely onaccurate measurement of PPG and ACC waveforms, along with anelectrocardiogram waveform (ECG), from patients that are both moving andat rest. cNIBP is typically measured with the ‘Composite Technique’,which is described in detail in the co-pending patent applicationentitled: VITAL SIGN MONITOR FOR MEASURING BLOOD PRESSURE USING OPTICAL,ELECTRICAL, AND PRESSURE WAVEFORMS (U.S. Ser. No. 12/138,194; filed Jun.12, 2008), the contents of which are fully incorporated herein byreference.

As described in these applications, the Composite Technique (or,alternatively, the ‘Hybrid Technique’ referred to therein) typicallyuses a single PPG waveform from the SpO2 measurement (typically theIR(PPG) waveform, as this typically has a better signal-to-noise ratiothan the RED(PPG) waveform), along with the ECG waveform, to calculate aparameter called ‘pulse transit time’ (PTT) which strongly correlates toblood pressure. Specifically, the ECG waveform features a sharply peakedQRS complex that indicates depolarization of the heart's left ventricle,and, informally, provides a time-dependent marker of a heart beat. PTTis the time separating the peak of the QRS complex and the onset, or‘foot’, of the RED/IR(PPG) waveforms; it is typically a few hundredmilliseconds. The QRS complex, along with the foot of each pulse in theRED/IR(PPG), can be used to more accurately extract AC signals using amathematical technique described in detail below. In other embodimentsboth the RED/IR(PPG) waveforms are collectively processed to enhance theaccuracy of the cNIBP measurement. The electrical system for measuringSpO2 features a small-scale, low-power circuit mounted on a circuitboard that fits within the wrist-worn transceiver. The transceiver canfurther include a touchpanel display, barcode reader, and wirelesssystems for ancillary applications described, for example, in thefollowing applications, the contents of which have been previouslyincorporated by reference: BODY-WORN MONITOR FEATURING ALARM SYSTEM THATPROCESSES A PATIENT'S MOTION AND VITAL SIGNS (U.S. Ser. No. 12/469,182;filed May 20, 2009) and BODY-WORN VITAL SIGN MONITOR WITH SYSTEM FORDETECTING AND ANALYZING MOTION (U.S. Ser. No. 12/469,094; filed May 20,2009).

In one aspect, the invention provides a system and method for monitoringa physiological property of a patient's blood (e.g. a SpO2 value). Theinvention features a first sensor with two radiation sources that emitoptical radiation at first and second wavelengths, and a photodetectorconfigured to detect the radiation after it passes through a portion ofthe patient. A finger-ring housing that houses the radiation sources andthe photodetector features a ring-shaped mounting portion that fits orwraps around a base of the patient's thumb. A processing unit, worn onthe patient's wrist and operably connected to the finger-ring sensor,receives signals from the photodetector and includes both a motionsensor and a processor. The processor is configured to process: i) thefirst and second signals to determine AC signals; ii) at least one ofthe AC signals and the motion signal to determine selected AC signals;and iii) the selected AC signals, or signals derived therefrom, todetermine the physiological property of the patient's blood.

In certain embodiments, the mounting portion comprises a curved,ring-shaped portion that partially surrounds the base of the patient'sthumb, while leaving the tip uncovered. The ring-shaped portion canconnect to a flexible strap made of, e.g., nylon or fabric. Typicallythe first and second radiation sources are proximal to one another (andare often within the same electronic package) and are separated from thephotodetector by an angle between 75-110 degrees. In these and otherembodiments the processing unit includes an input port (e.g. a serialport operating a serial protocol, such a control area network, or CANprotocol) configured to receive an electrical signal (e.g. a digitizedECG signal). The ECG signal, for example, is generated by a series ofbody-worn electrodes connected to differential amplifier circuit. Thecable that supplies the ECG signal can include this circuit, and canplug directly into the serial input port. The ECG signal includes atime-dependent marker (e.g. a QRS complex) that precedes both the firstand second PPG waveforms generated by each heartbeat by less thanpre-determined time period (e.g. 500 ms). It can be processed todetermine heart rate, and can additionally be processed to detect boththe AC and DC signals within the PPG waveforms, along with motion thatmay disrupt them. If motion is detected, the system can be instructed tosimply ignore the AC and DC components; this is typically done if motionexceeds a pre-determined level known to corrupt these signals beyond anacceptable level. Alternatively, if motion is present but is less thanthe pre-determined level, its influence over the AC and DC componentsmay be removed using frequency-domain filtering, deconvolution, orsimilar techniques.

In other embodiments both cNIBP and SpO2 are simultaneously detectedfrom both the PPG and ECG signals. cNIBP is determined, for example,from a time difference between a peak of a QRS complex in the ECG signaland an onset point in one of the AC signals. The time difference, forexample, is PTT, and cNIBP is determined according to the CompositeTechnique. In this case, cNIBP is most accurately determined when thefinger-ring sensor is worn on the base of the patient's thumb. For thisconfiguration, parameters relating ratios of the AC and DC signals toSpO2 need to be determined beforehand using, e.g., conventional breathedown studies.

In another aspect, the invention provides a method for simultaneouslymeasuring both SpO2 and a motion-related event (e.g. a patient'sposture, activity level, and degree of motion) from the patient.Typically posture may be measured with a single sensor (e.g. an analogor digital three-axis accelerometer) mounted on the patient's torso. Theaccelerometer can be mounted alongside the ECG circuit in a terminalportion of the ECG cable. In this embodiment, posture is typicallydetermined from a vector corresponding to orientation of the patient'storso. Specifically, an angle separating the vector from apre-determined coordinate system ultimately yields posture, as isdescribed in detail below. Activity level (corresponding, e.g., tomoving, walking, falling, convulsing) is another motion-related eventdetermined in this embodiment. It can be calculated from a mathematicaltransform of time-dependent variations of a motion signal that yields afrequency-domain spectrum. Portions of the spectrum (e.g. the power ofspecific frequency components) are compared to pre-determinedfrequency-dependent parameters to determine the activity level. Otheroperations, such as a mathematical derivative of the time-dependentmotion signal, or a series of ‘decision rules’ based on a decision-treealgorithm, can also yield the activity level.

In another aspect, the invention provides a complete body-worn vitalsign monitor for measuring all the patient's vital signs, includingSpO2, cNIBP, and oscillometric blood pressure (SYS, DIA, and MAP).Typically in this embodiment the body-worn monitor features a wrist-wornprocessing unit that includes, for example, multiple input ports tooperably connect with stand-alone systems for measuring some of thevital signs (e.g. to receive cables associated with systems formeasuring ECG and oscillometric blood pressure). Additional ports mayalso be used to collect signals from external sensors that measure,e.g., glucose level, respiration rate, and end-tidal CO₂. To simplifydata collection, each port typically operates on a common communicationprotocol, such as the CAN protocol. Input ports corresponding to ECG andoscillometric blood pressure are typically located on a common side ofthe processing unit that typically faces away from the patient's hand.In this embodiment any cable connecting to an input port may include anaccelerometer to characterize the patient's motion.

In certain embodiments the processing unit features a touchpanel displaythat renders a first user interface that displays information describingoxygen saturation, a second user interface that displays informationdescribing blood pressure, and a third user interface that displaysinformation describing ECG signals. The processing unit can also includea barcode scanner that scans a barcode of a medical professional(located, e.g., on their badge). In response, the wrist-worn transceivercan render a user interface corresponding to the medical professional.This prevents the patient from viewing medical information that may, forexample, cause unnecessary alarm.

In other embodiments the processing unit includes a speaker for voicecommunications, or for generating audible voice messages intended forthe patient. The processing unit can also include a wireless transmitterthat communicates through, e.g., a hospital network.

In another aspect, the invention provides a method for measuring SpO2and cNIBP by processing ECG, PPG, and motion signals with filters thatanalyze both PTT and AC signals of the PPG waveforms with a mathematicalfilter. The filter, for example, can be a ‘matched filter’, described indetail below. Typically only signals that are generated when motion isrelatively low are considered in this embodiment. For example, signalsare typically not processed further when the motion sensors indicatethat motion is greater than an acceptable level. Filtering PTT valuesincludes, for example, determining values that lie outside apre-determined range using statistical filters (e.g. a simple averageand standard deviation). More sophisticated techniques, such ascalculating a power value from a frequency-domain spectrum correspondingto the time-dependent motion signal, and then comparing this to apre-determined value, can be used to estimate if either the PTT valuesor PPG signals are affected by motion. SpO2 values are typicallycalculated from ratios describing the AC and DC signals measured fromindividual pulses using optical systems operating in the red andinfrared optical spectral regions. One or more ratios can be calculatedfor the pulse.

In another aspect, the invention provides a method for suppressing analarm/alert based on the SpO2 value by processing the patient's postureand activity state. For example, the alarm can be suppressed if thepatient's posture is upright (e.g. standing up), as patients having thisposture are typically not in immediate need of medical assistance.Similarly, the alarm can be suppressed if the patient's posture changesfrom lying down to sitting or standing up (or, alternatively, the otherway around). In this case the change in posture, which can be determinedwith the chest-worn accelerometer, may disrupt the PPG waveforms to thepoint where an alarm/alert would be falsely generated in the absence ofsuch alarm suppression.

Still other embodiments are found in the following detailed descriptionof the invention, and in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic drawing of a pulse oximeter probe configured asa finger-ring sensor worn around the base of a patient's thumb;

FIG. 2 shows an expanded, schematic drawing of the pulse oximeter probeof FIG. 1 with a dual-wavelength LED and photodetector separated fromthe finger-ring sensor;

FIG. 3A is a schematic drawing of a patient's thumb with the pulseoximeter probe of FIG. 2 attached;

FIG. 3B shows a cross-sectional, schematic drawing of a patient's thumbmeasured with the pulse oximeter probe of FIG. 3A;

FIG. 4 is a graph showing a relationship between SpO2 and a ‘ratio ofratios’ (RoR) for measurements from theoretical model in a medicaltextbook, made from the tip of a patient's index finger, and made fromthe base of a patient's thumb;

FIG. 5 is a graph comparing measurements from 20 unique patients madewith the thumb-worn pulse oximeter probe of FIG. 1 to those made at thetip of the index finger with a commercial oximeter probe;

FIG. 6 is a graph showing time-dependent waveforms measured with anaccelerometer (ACC), ECG system (ECG), and the 660 and 905 nm LEDs(RED(PPG) and IR(PPG), respectively) within the pulse oximeter probe ofFIG. 1;

FIG. 7 is a flow chart describing an algorithm for measuring SpO2 andpatient motion with the pulse oximeter probe of FIG. 1;

FIG. 8 is a graph showing data collected during a breathe down studyindicating correlation between SpO2 measured with the pulse oximeterprobe shown in FIG. 2 and SpO2 measured with a blood gas analyzer, whichin this case represents a ‘gold standard’;

FIGS. 9A and 9B are graphs, respectively, of time-dependent IR(PPG),ECG, and ACC waveforms measured when a patient is not undergoing motion;

FIGS. 10A and 10B are graphs, respectively, of time-dependent IR(PPG),ECG, and ACC waveforms measured when a patient is undergoing minorfinger motion;

FIGS. 11A and 11B are graphs, respectively, of time-dependent IR(PPG),ECG, and ACC waveforms measured when a patient is undergoing majorfinger motion;

FIG. 12A is a graph of time-dependent RED/IR(PPG), ECG, and ACCwaveforms similar to those shown in FIGS. 9A,B-11A,B measured duringperiods of motion and no motion;

FIGS. 12B and 12D are graphs of the IR(PPG) waveforms of FIG. 12Ameasured, respectively, during periods of motion and no motion, alongwith a graphical indication of how multiple RoRs are calculated from thewaveforms;

FIG. 12C is a graph of data points representing RoRs between componentsof the RED(PPG) and IR(PPG) waveforms measured during periods of nomotion and at periodic intervals between the foot and peak of thewaveform shown in FIG. 12B;

FIGS. 12E and 12F are graphs of data points representing RoRs betweencomponents of the RED(PPG) and IR(PPG) waveforms measured during periodsof motion at periodic intervals between the foot and peak of thewaveform shown in FIG. 12D;

FIG. 13A shows a graph of time-dependent ECG and IR(PPG) waveforms thatare processed to determine a PPT;

FIGS. 13B and 13C are graphs, respectively, of PTT versus heartbeatbefore and after applying a matched filter to the IR(PPG) waveform usedto calculate PTT as shown in FIG. 13A;

FIG. 13D is a flow chart showing an algorithm for analyzing PTT andapplying a matched filter to an IR(PPG) waveform when motion-relatednoise is present in the waveform;

FIG. 14 shows a graph of time-dependent waveforms (ECG, PPG, and ACC)generated from a resting patient by, respectively, the ECG system, theoptical system, and the accelerometer system of FIG. 1;

FIG. 15 shows a graph of time-dependent waveforms (ECG, PPG, and ACC)generated from a walking patient by, respectively, the ECG system, theoptical system, and the accelerometer system of FIG. 1;

FIG. 16 shows a graph of time-dependent waveforms (ECG, PPG, and ACC)generated from a convulsing patient by, respectively, the ECG system,the optical system, and the accelerometer system of FIG. 1;

FIG. 17 shows a graph of time-dependent waveforms (ECG, PPG, and ACC)generated from a falling patient by, respectively, the ECG system, theoptical system, and the accelerometer system of FIG. 1;

FIG. 18 is a schematic drawing of a coordinate system assigned to apatient wearing three accelerometers and a pulse oximeter probe;

FIG. 19A is a graph showing time-dependent ACC waveforms correspondingto different posture states and measured with an accelerometerpositioned on a patient's chest;

FIG. 19B is a graph showing posture states calculated using thetime-dependent ACC waveforms of FIG. 19A and a mathematical model fordetermining a patient's posture;

FIG. 20 is an electrical timing diagram showing how the 660 nm and 905nm LEDs are driven in the pulse oximeter probe described in FIG. 1;

FIG. 21 is an electrical diagram of a circuit used to drive the 660 nmand 905 nm LEDs according to the timing diagram shown in FIG. 20;

FIG. 22 is an electrical diagram of a circuit used to amplify and filterthe RED/IR(PPG) waveforms to generate the RED/IR(AC) and RED/IR(DC)components used to calculate SpO2;

FIGS. 23A and 23B show images of the body-worn monitor of FIG. 1attached to a patient with and without, respectively, a cuff-basedpneumatic system used for calibrating the cNIBP measurement; and

FIG. 24 shows an image of the wrist-worn transceiver featured in thebody-worn monitor of FIGS. 23A and 23B.

DETAILED DESCRIPTION OF THE INVENTION

FIGS. 1 and 2 show a pulse oximeter probe 1 shaped as a finger ring thatwraps around a base of patient's thumb 3 to measure SpO2 and cNIBP. Theprobe 1 is designed to be comfortably worn for extended periods (e.g.several days) while freeing up the patient's thumb and hands foractivities such as reading and eating that are commonplace in, e.g., ahospital. Motion corresponding to these and other activities can affectthe SpO2 measurement and is detected with a network of accelerometersworn on the patient's body. The probe 1 makes a transmission-modeoptical measurement along an inner portion of the thumb 3 with a pair ofembedded LEDs 9, 10 operating at, respectively, 660 and 905 nm, and asingle photodetector 12 that detects these wavelengths after they passthrough vasculature and other tissue lying beneath the LEDs 9,10.Specifically, both LEDs 9, 10 and the photodetector 12 are positioned tomeasure blood pulsing in portions of the princeps pollicis artery, whichis the principal artery of the thumb and stems from the radial artery.As described in detail below, measuring blood flowing in this arteryenhances the accuracy of the cNIBP measurement. A small circuit board 11supports the photodetector 12 and may additionally include, for example,a trans-impedance amplifier for amplifying photocurrent from thephotodetector 12 and converting it into a corresponding voltage. Thecircuit board 12 also includes a resistor 19 that identifies specificwavelengths emitted by the LEDs 9, 10; these wavelengths, in turn,influence values of correlation coefficients that relate RoR to SpO2, asdescribed below. Some of these circuit elements are described in moredetail below with reference to FIGS. 21 and 22.

A ring-shaped, flexible plastic housing 6 formed into a cylindricalradius of curvature features rectangular openings 18, 22 that supportthe LEDs 9, 10 and circuit board 11. The housing 6 features threecut-out portions 23A-C, or ‘living hinges’, that make it easily bendableand able to accommodate thumbs of difference sizes. It is held in placearound the base of the thumb with a flexible nylon strap 2 threadingthrough two D-ring openings 13A, 13B located the housing's distal ends.A portion 4 of the strap 2 features a patch 17 of Velcro (containing,e.g., ‘hooks’) that adheres to a mated patch 16 (containing, e.g.,‘loops’) on the strap's main portion; the patches 16, 17 temporarilyadhere to each other when the housing 6 is worn on the patient's thumb3, and easily detach so that it can be removed. The straps 2, 4 allowthe probe 1 to be securely fastened, which in turn minimizes motionrelative to the measurement site. A flexible, cable 5 connects theoximeter probe 1 to a wrist-worn transceiver, similar to the one shownin FIG. 24. The cable 5 carries I/O signals from the wrist-worntransceiver that drive the LEDs according to the timing diagram in FIG.20, and analog signals measured by the photodetector to anamplifying/filtering circuit within the transceiver, as shown in FIG.22. There, the analog signals are amplified, filtered, digitized, andprocessed to measure SpO2, as described in detail below.

As shown in FIGS. 3A and B, during a pulse oximetry measurement the LEDs9, intermittently emit beams 28, 29 of radiation at 660 nm and 905 nm atroughly 500 Hz according to the timing diagram in FIG. 20. Once emitted,the beams 28, 29 pass through the base of the thumb 3 and rapidlydiverge to scatter off tissue such as skin 24, bone 25, capillaries 26near the thumb's outer surface, and a portion of the princeps pollicisartery 27 before reaching the photodetector 12. To increase the amountof radiation that passes through the artery 27 and capillaries 26, andthereby optimize signal quality, the LEDs 9, 10 and photodiode 12 areseparated by approximately 35-55 degrees. Optical components separatedat this angle tend to increase the relative contribution of signalcoming from the artery 27; ultimately this improves the accuracy of thecNIBP measurement, as PTT values measured from arterial componentscorrelate better to blood pressure than those measured from capillarycomponents. Both the capillaries 26 and princeps pollicis artery 27carry blood that pulsates with each heartbeat and absorbs radiationemitted by the LEDs 9, 10. This results in separate time-dependentoptical waveforms (i.e. RED/IR(PPG), shown in FIG. 6, generated by the660 and 905 nm radiation. Both waveforms feature AC signalscorresponding to the time-dependent pulsating blood, and DC signalscorresponding to time-independent scattering off the skin 24, bone 25,and non-pulsating components of the capillaries 26 and artery 27. Priorto any filtering the AC component typically represents about 0.5-1% ofthe total signal.

Collectively processing both the AC and DC signals of the RED/IR(PPG)waveforms yields a SpO2 value. The body-worn monitor calculates thesecomponents using a number of signal-processing methodologies that areadditionally important for determining PTT-based cNIBP. Ultimately theAC and DC components yield a RoR which then relates to a SpO2 using aseries of empirically determined coefficients. In one embodiment, forexample, the RoR is determined by first measuring RED/IR(PPG) waveforms,and then passing them through a low-pass filter characterized by a 20 Hzcutoff. The averaged baseline components of each waveform are sampledand stored in memory, and represent RED/IR(DC). Both waveforms are thenadditionally filtered with high-pass filter having a 0.1 Hz cutofffrequency, which is typically implemented with a finite impulse responsefunction, and finally amplified with a variable gain amplifier. Thesesteps can be done with either analog electronic or digital softwarefilters. Components passing through this filter are isolated asdescribed below with reference to FIGS. 6 and 7 to yield RED/IR(AC).Once determined, the AC and DC signals are processed to yield a RoRvalue, described in equation (3), which relates to SpO2:

$\begin{matrix}{{RoR} = \frac{{{RED}({AC})}/{{RED}({DC})}}{{{IR}({AC})}/{{IR}({DC})}}} & (3)\end{matrix}$

FIG. 4 shows an empirical relationship between RoR and SpO2 formeasurements made at the base of the thumb with the oximeter probe shownin FIGS. 1 and 2 (solid line), along with similar relationships formeasurements made at the tip of the index finger with an off-the-shelfoximeter probe (small dashes), and the theoretical curve formeasurements made from the tip of the index finger (large dashes).Curves corresponding to measurements made from the index finger andthumb are determined empirically from a group of patients measured undersimilar conditions. As is clear from the figure, the relationshipsbetween RoR and SpO2 are similar, but slightly offset due to differencesin the measurement site. Without being bound to any theory, thesedifferences may be due to the relatively low density of capillary bedsnear the base of the thumb as compared to those in the tip of the indexfinger. The relationship for all curves in FIG. 4 is non-linear,particularly for SpO2 values ranging from about 70-100%. Values below70% can be accounted for with a different non-linear model, such as onebased on a second-order polynomial. Coefficients a, b, and c for thismodel are determined by fitting the empirical data to a correspondingmathematical function like the second-order polynomial shown in equation(4) below:

SpO2=(a+b*RoR+c*RoR ²)×100  (4)

Optimized values for a, b, and c coefficients corresponding tomeasurements made at the base of the thumb are shown in Table 1, below:

TABLE 1 coefficients for equation 4 relating RoR to SpO2 formeasurements made at the base of the thumb Parameter Value a 107.3 b−3.0 c −20.0

The exact values of parameters shown in Table 1 will depend of thespecific wavelengths of the LEDs used in the pulse oximeter probe. Thisis because the SpO2 measurement is fundamentally determined by therelative optical absorption of Hb and HbO2 in the red and infraredspectral regions, and absorption, in turn, will depend on the wavelengthemitted by the LEDs. The absorption spectra of Hb and HbO2 arerelatively flat in the infrared spectral region, but strongly diverge inthe red spectral region. The coefficients shown in Table 1 are thusrelatively sensitive to the exact wavelength of the red LED. For thisreason, a series of empirical studies need to be performed using pulseoximeter probes featuring LEDs of varying wavelengths surrounding thered emission wavelength (e.g. 600-610 nm) prior to manufacturing. Such astudy, for example, is described with reference to FIG. 8. It istypically classified as a ‘breathe down’ study because it involveslowering the SpO2 values of a series of patients (typically about 10-15)under medical supervision. SpO2 is typically lowered by decreasing theamount of oxygen each patient inhales through a specialized ventilatormask; this is often done in a room with a reduced temperature. Bloodfrom the patients is extracted from an arterial line and analyzed with ablood gas analyzer to determine its oxygen content. Simultaneously, apulse oximeter probe with known LED wavelengths is attached to eachpatient (in this case at the base of the thumb) and used to measure theRoR described in equation (3). SpO2 values for this experiment, asmeasured with the blood gas analyzer, typically range from 70-100%.Simultaneous studies are typically done using pulse oximeter probeshaving LEDs with different red emission spectra. Upon completion of thestudies, the wavelength-dependent values of RoR are related to SpO2, asdetermined by the blood gas analyzer, to calculate coefficients a, b, cas described in Table 1. In general, a different set of coefficientswill result for the different LED wavelengths. These coefficients andthe optical wavelengths they correspond to, along with a resistor valuedescribed below, are stored in a database in memory on the wrist-worntransceiver.

Prior to manufacturing of the pulse oximeter probe (e.g. the probe shownin FIG. 1), the wavelengths of the LEDs are determined, typically with aresolution of about 1 nm, using an emission spectrophotometer. LEDs arethen sorted by wavelength and associated with a resistor having a valuethat is stored in the above-described database. During manufacturing,the resistor is soldered to the circuit board (and optionally trimmedwith a laser to give it a well-defined resistance) within the pulseoximeter probe shown in FIG. 1. During an actual measurement, thewrist-worn transceiver delivers a voltage through the cable connectingit to the pulse oximeter probe that decreases in value after passingthrough the resistor. This voltage drop is sensed by the transceiverusing the analog-to-digital converter and processor, and then used tocalculate the resistor value. The value is compared to the database, andultimately used to select the appropriate a, b, and c coefficientsdescribed above. Ultimately this maximizes accuracy of the SpO2calculation.

Measurements made at the base of the thumb provide accurate SpO2 valuesand increase patient comfort. Additionally, the IR(PPG) measured fromthis site, when processed in combination to the ECG waveform, yields aPTT value that can be processed with the Composite Technique to yield anaccurate cNIBP measurement. As described above, an IR(PPG) waveformmeasured from primarily from the princeps pollicis artery increases theaccuracy of the cNIBP measurement. With an initial pressure-basedcalibration (performed e.g. by the pneumatic system 285 shown in FIG.23A) SYS and DIA can be explicitly determined for each heartbeat usingan algorithm described in the above-mentioned patent applications, thecontents of which have been previously incorporated herein by reference.Typically, PTT values are processed over a 20-40 second time period(often implemented as a ‘rolling average’) using statistical filteringto improve accuracy. To better define the onset of the PPG waveform, andthus improve the accuracy to which SYS and DIA are determined, timescorresponding to RED(foot) and IR(foot) are typically averaged together.When compared to SYS and DIA values measured under clinical conditionswith a femoral arterial line, cNIBP measurements made from thisparticular location were well within the FDA's standards for accuracy(+/−5 mmHg) and standard deviation (8 mmHg). For this and other reasonsthe base of the thumb appears to be a uniquely good location formeasuring both SpO2 and cNIBP.

FIG. 5 shows a direct comparison between SpO2 measured from the base ofthe thumb and tip of the index finger from a group of 20 separatepatients. Each patient was measured for a 30-second period from the tipof finger with a commercially available oximeter probe, and then for acomparable period from the base of the thumb with an oximeter probesubstantially similar to that shown in FIGS. 1 and 2. During themeasurement an average value for SpO2 was detected from each location.For these patients the relationships between RoR and SpO2 shown in FIG.4 were used for both sets of measurements. As is clear from the data,the correlation for these measurements is within experimental error(estimated at 1% SpO2 for each measurement) for all 20 patients. Themean difference between the two measurements (thumb SpO2−index fingerSpO2) is −0.6%O2, and the standard deviation of the differences is1.39%O2. Measurements were made over a range of 93-100%O2.

The pulse oximeter system described above is integrated into a complete,body-worn monitoring system, shown in FIGS. 23A and 23B, that measuresand analyzes ECG, ACC, RED(PPG) and IR(PPG) waveforms to characterizethe patient's vital signs, degree of motion, activity level, andposture. FIG. 6, for example, shows the various time-dependent waveformsmeasured concurrently over a 3-second period using such a system. TheACC waveform represents a measurement along a single axis made by one ofthe three accelerometers incorporated in the body-worn monitor.Typically this is the accelerometer included in the wrist-worntransceiver, as this site is closest to the measurement site for SpO2.SpO2 values are determined by measuring the red and infrared waveformsbetween the peak and foot values, indicated by colored triangles in thefigure and described in more detail below. Typically these peak valuesare determined by filtering each waveform as described above, and thentaking a first derivative and evaluating a zero-point crossing,indicating the point where the waveform's slope changes from a positiveto negative value. Values for these parameters can be averaged overseveral pulses and processed with statistical techniques (e.g. averagingafter excluding values outside of +/−1 standard deviation) to yield theRED/IR(peak) values for individual pulses within the RED/IR(PPG)waveforms. RED/IR(foot) values for each pulse are used for PTT-basedcNIBP measurements, and are typically processed with statisticaltechniques as described above. The foot of each waveform is bestcalculated by measuring a peak from the second derivative of theRED/IR(PPG) waveforms. RED/IR(DC) values (not shown in the figure) aretypically determined by sampling RED/IR(PPG) waveforms after passinganalog versions of these waveforms through a low-pass filter asdescribed above.

Electrodes adhered to the patient's chest and connected to an ECGcircuit in the body-worn monitor measure a three-lead ECG, with FIG. 6showing a time-dependent waveform taken from Lead II. The ECG waveformfeatures a series of QRS complexes, indicated by the black triangles,with each QRS complex corresponding to a single heartbeat. The QRScomplex typically proceeds each pulse in the RED/IR(PPG) by betweenabout 100-200 milliseconds, and is easy to detect because of its sharp,well-defined features. Moreover, as described in more detail below withreference to FIGS. 9A,B-11A,B, the ECG waveform and its associated QRScomplexes are relatively insensitive to motion, in contrast the both theRED/IR(PPG) waveforms. This means each QRS complex can serve as a markeror ‘fiducial’ for detecting AC signals in the two PPGs. Specifically,during a measurement an algorithm operating on the wrist-worntransceiver detects a QRS complex (e.g. ECG QRS-N) and then sequentiallyanalyzes both the RED/IR(PPG) waveforms at times within 500 ms of thisfeature. If RED/IR(foot) and RED/IR(peak) cannot be determined duringthis time interval using the first and second derivative tests describedabove, the algorithm determines that they are immeasurable (most likelybecause of noise in the PPG), and begins searching for similar valuescorresponding to the next heartbeat. If the ACC waveform indicates ahigh degree of motion, then the RED/IR(PPG) waveforms are processed asdescribed below to determine if foot and peak parameters can beextracted from them. If not, the waveforms are determined to beimmeasurable, the first and second derivative tests are not performed,and the ECG and PPG waveforms corresponding to the N+1 heartbeat areinvestigated. This process is repeated, typically for a total time ofbetween 5-10 seconds, for as many heartbeats as possible.

If no more than an acceptable level of motion is present, as indicatedby the ACC waveform, then the values for RED/IR(foot) and RED/IR(peak)can be processed to determine an RoR for each heartbeat. This value canthen be analyzed with the statistical techniques described above tolimit artifacts and ultimately generate a SpO2 value with the greatestpossible accuracy. In one embodiment, to convert the RED/IR(peak) andRED/IR(foot) values into AC values used for the RoR calculation, theamplitude of each pulse in the RED/IR(PPG) waveforms is calculated asshown in equations (5) and (6) below:

RED(AMP)=RED(peak)−RED(foot)  (5)

IR(AMP)=IR(peak)−IR(foot)  (6)

The DC values for the RED/IR(PPG) waveforms are then determined by firstcalculating any DC offset values (RED/IR(DC)) supplied to thedifferential inputs of the analog-to-digital converter; these inputs areindicated, for example, by RED/IR(DC) labels in FIG. 22. Once digitized,these values can then be used to calculate an ‘effective’ DC value,RED/IR(DC*), for both the red and infrared wavelengths, as describedbelow in equations (7) and (8). Ambient light, defined as AMBIENT(DC),is accounted for by measuring any radiation incident on thephotodetector when neither the red or infrared LEDs are turned on (thisoccurs, for example, between current pulses that drive the respectiveLEDs, as shown in FIG. 20).

$\begin{matrix}{{{{RED}\left( {DC} \right.}{*)}} = {\frac{{{RED}({peak})} + {{RED}({foot})}}{2} + {{RED}({DC})} - {{AMBIENT}({DC})}}} & (7) \\{{\mspace{79mu} {{IR}\left( {DC} \right.}{*)}} = {\frac{{{IR}({peak})} + {{IR}({foot})}}{2} + {{IR}({DC})} - {{AMBIENT}({DC})}}} & (8)\end{matrix}$

An RoR value is then determined from equations (5)-(8):

$\begin{matrix}{{RoR} = \frac{{{{RED}({AMP})}/{{RED}\left( {DC} \right.}}{*)}}{{{{IR}({AMP})}/{{IR}\left( {DC} \right.}}{*)}}} & (9)\end{matrix}$

The ACC waveform in FIG. 6 provides an accurate indication of thepatient's motion. Typically this waveform, along with two otherscorresponding to additional axes of a coordinate system, is sensed witha solid-state device called an accelerometer. An accelerometer istypically a micro electrical-mechanical system (MEMS) device thatmeasures the acceleration of the part of the body that it is attachedto. The ACC waveform measured by the accelerometer features DC valuesthat indicate the position of the accelerometer relative to a gravityvector, and AC values that indicate movement-induced acceleration.Suitable accelerometers typically have a response times less than about1 microsecond, and are thus more than adequate for detecting most typesof patient motion, which typically occurs in less than 15 Hz. Processingthe ACC waveforms yields at least three valuable pieces of informationrelevant to the SpO2 measurement: it can determine i) a patient'sposture; ii) their activity state (e.g. are they lying down, walking,sitting, standing); and iii) whether or not the patient's hand inmoving, thereby indicating that the IR/RED(PPG) waveforms may becorrupted by noise, and thus more likely to yield erroneous values forSpO2. Additional processing of the ACC waveforms yields the patient'sarm height, from which hydrostatic changes in blood pressure can beestimated and used to calibrate the cNIBP measurement. The process fordetecting each of these scenarios is summarized below, and in detail inthe following patent applications, the contents of which have been fullyincorporated herein by reference: BODY-WORN MONITOR FEATURING ALARMSYSTEM THAT PROCESSES A PATIENT'S MOTION AND VITAL SIGNS (U.S. Ser. No.12/469,182; filed May 20, 2009) and BODY-WORN VITAL SIGN MONITOR WITHSYSTEM FOR DETECTING AND ANALYZING MOTION (U.S. Ser. No. 12/469,094;filed May 20, 2009).

FIG. 7 shows a flow chart describing an algorithm 33 that collectivelydetermines SpO2, SYS, and DIA, along with a patient's motion, posture,and activity level, by processing the time-dependent RED/IR(PPG), ECG,and ACC waveforms shown in FIG. 6. The algorithm 33 begins withmeasuring the ECG, RED/IR(PPG), and ACC waveforms (nine total waveformsmeasured with three separate accelerometers) using a body-worn monitor,similar to that shown in FIGS. 23A, 23B (step 35). RED/IR(PPG) waveformsare measured with the pulse oximeter probe described above, and the ECGwaveform is measured with an ECG circuit that terminates an ECG cableand attaches to at least three electrodes worn on the patient's chest.Three accelerometers are typically disposed, respectively, in thewrist-worn transceiver, in a cable that attaches to the patient's bicep,and on the chest proximal to the ECG circuit. A series of analog filters(performed with hardware components described with reference to FIG. 22)and digital filters (performed in software using well-knownfrequency-domain techniques, such as using a ‘matched filter’, describedbelow) process each waveform to remove unwanted and relativelyhigh-frequency electrical and mechanical noise (step 36). Suchfiltering, for example, increases the accuracy to which data pointscorresponding to IR/RED(peak) and IR/RED(foot) can be determined.Typically RED/IR(DC) values are determined directly from waveforms thatpass through a low-pass filter characterized by a 20 hz cutofffrequency, as described in equations (7) and (8), above (step 47). Afterfiltering, the ECG QRS for each heartbeat is detected with abeat-picking algorithm described, for example, in the followingreference, the contents of which are incorporated herein by reference(step 37): ‘ECG Beat Detection Using Filter Banks’, Afonso et al., IEEETrans. Biomed Eng., 46:192-202 (1999). Heart rate is typicallydetermined from the inverse of time separating neighboring QRScomplexes. Degree of motion, posture, and activity level are thendetermined from the nine ACC waveforms, as described in detail in theabove-referenced patent applications, and briefly below (step 38). Eachof these parameters features its own unique classification. For example,degree of motion can be ranked numerically depending on the magnitude ofacceleration detected from one or more accelerometers. Posture istypically divided into well-known categories such as standing, lying onback, lying prone, etc. Activity levels include activities such aswalking, falling, resting, and convulsing.

If the algorithm determines that no significant motion is present (step39), it proceeds to process the RED/IR(PPG) waveforms, starting at atime corresponding to the ECG QRS generated by a first heartbeat, andcontinuing the processing until a pre-determined time delta (e.g. 500ms) or a time corresponding to a neighboring heart beat is reached (step41). As used herein, ‘significant motion’ refers to an amount of motionthat would render the RED/IR(PPG) waveforms unreliable for calculationof SpO2. This processing typically involves further digitally filteringthe waveforms to remove any high-frequency noise, and then determiningIR/PPG(peak) values from the first derivative of each waveform, andIR/PPG(foot) values from the second derivative of each waveform (step42). If during step 39 motion is determined to be present, the algorithmproceeds to analyze both RED/IR(PPG) waveforms to determine if they aredistorted in any way (step 40). Such analysis, for example, can involvecomplex methods such as comparing a pulse from one or both of the PPGwaveforms to a ‘known good pulse’ collected during a period ofrelatively low motion. In this case, the comparative method can be alinear or non-linear numerical fitting algorithm, such as aleast-squares or Levenburg-Marquardt algorithm, or based on a standardcorrelation algorithm that determines agreement between the immediatepulse and the known good pulse. This latter approach can be implementedas a ‘matched filter’, and is described in detail below with regard toequations (13) and (14). A matched filter algorithm, for example, ispreferably implemented in step 36 to improve the signal-to-noise ratioof both the PPG and, to a lesser extent, the ECG waveform prior toprocessing these signals. Or it could be implemented during step 40 todetermine a degree of correlation between the immediate pulse in the PPGwaveform and a known good pulse to determine if the immediate pulse iscorrupted by motion. A derivation of the matched filter is provided, forexample, in Turin, ‘An introduction to matched filters’, IRETransactions of Information Theory 6: 311-329 (1960), the contents ofwhich are incorporated herein by reference.

A relatively simple method for determining a known good pulse involves,for example, determining the standard deviation of the PPG waveformduring a time period commensurate with the ECG QRS (at this point thepulse is not evident in the PPG waveform), and comparing this to apre-determined metric to estimate the motion-induced noise in thewaveform. If either PPG is determined to be significantly distorted bymotion, it is not included in the algorithm, and the processes ofcollecting and analyzing RED/IR(PPG), ECG, and ACC waveforms is repeated(steps 35-38, 47).

As described above, values for RED/IR(foot) and RED/IR(peak) can be usedto calculate both SpO2 and cNIBP. For the cNIBP calculation, RED/IR(PTT)values are determined from the time difference separating the ECG QRSand RED/IR(foot). Values corresponding to RED/IR(AC) are determined fromthe waveforms between the RED/IR(foot) and RED/IR(peak) values, asdescribed above in equation (9), and below in equations (10)-(11). Thisyields a RoR for each heartbeat-induced pulse in the waveforms. BothRED/IR(AC) and RED/IR(PTT) values are typically determined for eachheartbeat measured over a pre-determined period ranging from about 10-30seconds, and then subjected to a series of statistical tests (step 44)that typically involve taking the average and standard deviation of eachvalue over the time period. A ‘rolling average’ can also be used duringstep 44 so that fresh values are determined, e.g., every second. Valuesthat lie outside of one standard deviation from the average aretypically removed, and then the average is recalculated. The finalaverage value of PTT is then determined as the average of the averagedRED/IR(PTT) values, while the final average value of RED/IR(AC) isdetermined in a similar manner from the RoR values determined for eachpulse (step 45). The algorithm calculates cNIBP values of SYS and DIAdirectly from the averaged PTT value, as described in detail in theabove-referenced patent applications (step 46). Using equation (4), itcalculates SpO2 from the RoR values determined during step 45.

To accurately generate alarms/alerts when continuously monitoring apatient, it is often necessary to consider both the patient's vitalsigns and their motion. Thus, during step 50, an alarm/alert is onlygenerated from SYS, DIA, and SpO2 values after processing the patient'sdegree of motion, posture, and activity level (determined during step38). For example, if the patient is determined to be walking with anormal gate, it can be assumed that their values of SYS, DIA, and SpO2do not warrant an alarm/alert, even if one or all of these parametersexceeds a pre-determined alarm threshold. Conversely, an alarm/alert fora falling or convulsing patient would likely be generated even if thevalues for SYS, DIA, and SpO2 fall within the pre-determined alarmthresholds. Specific methodologies for alarms/alerts that consider bothvital signs and patient motion are found in the above-referenced patentapplications, the contents of which have been previously incorporated byreference.

As shown in FIG. 8, SpO2 data collected with the pulse oximeter probeshown in FIG. 2 and processed with the algorithm shown in FIG. 7correlate well with that analyzed with a blood gas analyzer, which inthis case represents a ‘gold standard’. Data shown in the figure werecollected during a conventional breathe down study, wherein SpO2 valuesof 15 healthy volunteers were systematically lowered from a normal valuenear 100%O2 to an abnormal value near about 70%O2 by carefullycontrolling the subjects' oxygen intake. In total, about 20 data pointswere measured for each subject over this range. Blood samples for theblood gas analyzer were extracted using an in-dwelling catheter, similarto a conventional arterial line, inserted in the subjects' radialartery. Data in the graph measured according to the invention describedherein (shown along the y-axis) correlate well with the gold standard(x-axis), yielding an r̂2 value of 0.9. The BIAS for this correlation is−0.3%O2, and the standard deviation is 2.56%O2. Data were collected andanalyzed with a prototype system, and indicate the efficacy of theinvention described herein. They are expected to further improve with aproduction-quality system. As is clear from the graph, correlation forrelatively low SpO2 values (e.g. those near 70%O2) is worse than thatfor relatively high SpO2 values (e.g. those near 95%); such ameasurement response is typical for commercially available pulseoximeters, and is primarily due to a decreasing signal-to-noise rationin the RED(PPG), which decreases with SpO2.

FIGS. 9A,B-11A,B indicate how different degrees of motion from apatient's fingers can influence both ECG and PPG signals, therebyaffecting the accuracy of both SpO2 and cNIBP measurements. In thesefigures the IR(PPG) is shown, as this signal typically has a bettersignal-to-noise ratio than that of the RED(PPG). The ACC waveform istypically measured along the vertical axis of the accelerometer embeddedin the wrist-worn transceiver. The magnitude of the axes for ECG, PPG,and ACC waveforms are the same for all figures.

In FIG. 9B, for example, the ACC waveform is relatively flat and lackingany significant time-dependent features, indicating that the patient isnot moving and is relatively still. Consequently the IR(PPG) in FIG. 9A,which is strongly affected by motion, features well-defined values forIR(foot), indicated by marker 53, and IR(peak), indicated by marker 54.Likewise the ECG waveform features a QRS complex, indicated by marker52, which is undistorted. The fidelity of these features indicate thatboth SpO2 and cNIBP values can typically be accurately determined duringperiods of little or no motion, as indicated by the ACC waveform in FIG.9B.

FIGS. 10A,B show how minor amounts of finger motion affect both the ECGand IR(PPG) waveforms. As shown by a portion of the dashed box 60 inFIG. 10B, finger motion is manifested by a time-dependent change in theACC waveform, the beginning of which is indicated by marker 62, whichpersists for a little less than one second. This is in stark contrast tothe portion of the ACC waveform in the preceding dashed box 56, whichindicates no motion. Finger motion has basically no effect on the ECGwaveform and its associated QRS complex, as indicated by marker 61. Butthe motion causes a minor amount of distortion of the IR(PPG) waveform.Specifically, when the motion is maximized (i.e. at the timecorresponding to marker 62) a small bump indicated by marker 57 appearsin the IR(PPG) waveform. This bump is an artifact that, in the absenceof the ACC waveform, could be misinterpreted as a pulse containing botha foot and peak in the IR(PPG) waveform. Additionally, the minor fingermotion distorts the foot (marker 58) and peak (marker 59) of thesubsequent pulse in the IR(PPG) waveform, making it difficult toaccurately determine these parameters using the derivative testsdescribed above. Thus, rather than erroneously interpreting thesefeatures and generating inaccurate values for both SpO2 and cNIBP, analgorithm described herein can disregard them based on the magnitude ofthe ACC waveform, and continue its calculation of vital signs once thefinger motion is reduced to an acceptable level.

FIGS. 11A,B show the affects of a major amount of finger motion on boththe ECG and IR(PPG) waveforms. Here, the period of motion is indicatedin both figures by the dashed box 65, which contrasts with the precedingperiod where motion is not present, shown in the dashed box 66. The ACCwaveform in FIG. 11B indicates the finger motion lasts for roughly onesecond, beginning and ending at times indicated, respectively, nearmarkers 68 and 69. The motion is complex and peaks in intensity atmarker 69. Even for major finger motion the ECG waveform and its QRScomplex, indicated by marker 67, are relatively undistorted. But theIR(PPG) measured during the period of motion is strongly distorted tothe point that its peak value, indicated by marker 64, is relativelyflat and basically immeasurable. This makes it difficult to accuratelymeasure IR(AC) and the subsequent SpO2 value calculated from thisparameter. The IR(foot) value, indicated by marker 63, is alsodistorted, but to a lesser degree than the corresponding peak value.

Data shown in FIGS. 9A,B-11A,B indicate that motion can be detected andaccounted for during pulse oximetry measurements to minimize theoccurrence of false alarms and, additionally, make accurate readings inthe presence of motion. For example, Equation (9), above, yields asingle RoR value for each pulse in the RED/IR(PPG) waveforms. Howeverthe method for calculating SpO2 based on a single value is limited, asonly one RoR can be calculated for each heartbeat; if values fromseveral heartbeats are averaged together it can thus take severalseconds to update the SpO2 value. And the single RoR value can bestrongly influenced by motion during pulses within the RED/IR(PPG)waveforms, as described above with reference to FIGS. 9A, B-11A,B.

Alternatively, RoR can be calculated using a method indicatedschematically in FIGS. 12A-F. In this method, multiple ‘sub-ratios’between AC components corresponding to RED/IR(PPGs) are calculated fromthe foot of each pulse to its corresponding peak, determined asdescribed above. The sub-ratios are calculated for each time interval ofα (typically 33 ms, corresponding to 30 Hz) and only during periods ofno motion, as determined from the ACC waveforms. Once a group ofsub-ratios are determined for a given pulse, they can be processed witha variety of statistical techniques, described in detail below, toestimate an accurate RoR for a given pulse. This RoR can then be furtherprocessed for multiple pulses. A pulse oximetry measurement based onsub-ratios has the advantage of being relatively accurate and having afaster update rate when compared to the method associated with equation(9), which calculates just a single RoR for each pulse in theRED/IR(PPG) waveforms. Such a measurement is described in more detail inthe following reference, the contents of which are incorporated hereinby reference: Wukitsch et al., ‘Pulse Oximetry: Analysis of Theory,Technology, and Practice’, Journal of Clinical Monitoring, 4:290-301(1988).

FIG. 12A shows ACC, ECG, and RED/IR(PPG) waveforms measured duringperiods of no motion (indicated by the dashed box 70) and motion(indicated by the dashed box 71). As with FIGS. 9A-11A, motion in FIG.12A is indicated by sharp, abrupt changes in the ACC waveform generatedalong a vertical axis by an accelerometer mounted in the wrist-worntransceiver. The ECG waveforms shown in both dashed boxes 70, 71 arerelatively immune to motion, and thus feature QRS complexes that can bemeasured easily. They serve as fiducial markers for analyzing theRED/IR(PPG) waveforms which, unlike the ECG waveform, are stronglyaffected by motion.

FIG. 12B shows the IR(PPG) from dashed box 70 in FIG. 12A, which ismeasured when the patient is not moving. In this case, the waveformfeatures a smooth, systematic rise time that is relatively easy toprocess with first and second derivative tests to determine its peak(indicated by marker 73) and foot (indicated by marker 72). Typicallysub-ratios are only calculated between these markers 72, 73, as they arecharacterized by a relatively large amplitude change in the RED/IR(PPG)and are more indicative of a patient's actual SpO2 value than sub-ratioscalculated in the second half of the waveform, characterized by arelatively gradual decrease in intensity. A single sub-ratio iscalculated from both the RED/IR(PPG) waveforms for each time interval α,as indicated by the dashed lines 78. FIG. 12C shows the resultingsub-ratios, indicated as dark circles with values of RoR(n) in thegraph; the values of each of these data points are indicated below byequation (10):

$\begin{matrix}{{{RoR}(n)} = \frac{\left\lbrack \frac{{{RED}\left( {{{PPG}\text{:}\mspace{14mu} n} + \alpha} \right)} - {{RED}\left( {{PPG}\text{:}\mspace{14mu} n} \right)}}{\begin{matrix}{{{RED}\left( {{{PPG}\text{:}\mspace{14mu} n} + {\alpha/2}} \right)} +} \\{{{RED}({DC})} - {{AMBIENT}({DC})}}\end{matrix}} \right\rbrack}{\left\lbrack \frac{{{IR}\left( {{{PPG}\text{:}\mspace{14mu} n} + \alpha} \right)} - {{IR}\left( {{PPG}\text{:}\mspace{14mu} n} \right)}}{\begin{matrix}{{{IR}\left( {{{PPG}\text{:}\mspace{14mu} n} + {\alpha/2}} \right)} +} \\{{{IR}({DC})} - {{AMBIENT}({DC})}}\end{matrix}} \right\rbrack}} & (10)\end{matrix}$

where RED/IR(DC) and AMBIENT(DC) are assumed to be constant throughoutthe entire pulse, and are described above.

Because the patient is not moving, the sub-ratios in FIG. 12C arerelatively constant and show little variation in the graph. They can bestatistically processed with a variety of statistical techniques, someof which are described in the above-mentioned reference, to determine an‘effective RoR’ for each pulse in the RED/IR(PPG) waveforms. Forexample, each value of RoR(n) from equation (10) can be processed with aweighted average defined by wt(n) to determine the effective RoR, asshown below in equation (11):

$\begin{matrix}{{{effective}\mspace{14mu} {RoR}} = \frac{\sum\limits_{n{foot}}^{n{peak}}{{{RoR}(n)}*{{wt}(n)}}}{\sum\limits_{n{foot}}^{n{peak}}{{wt}(n)}}} & (11)\end{matrix}$

In one embodiment, each weight wt(n) is determined by comparing an SpO2calculated from its corresponding RoR(n) to a preceding value for SpO2and determining the weight based on the correlation. For example, if thepreceding value for SpO2 is 98%O2, a value for SpO2 in the range of70-80%O2 calculated from RoR(n) is likely erroneous; the RoR(n) istherefore give a relatively low weight wt(n). Additionally, a relativelylarge change in the RED/IR(PPG) amplitude during the sub-ratiomeasurement period n typically indicates that the corresponding value ofRoR(n) has a relatively high accuracy. Such values are thus given arelatively high weight wt(n). In general, a number of establishedstatistical techniques can be used to weight the collection of RoR(n)values to generate the effective RoR, as defined above in equation (11).

In another embodiment, the collection of RoR(n) values, such as thoseshown in FIG. 12C, are processed to determine an average and standarddeviation. Values lying more than one standard deviation outside theaverage are then removed from the calculation, and the average is thenrecalculated. This technique, while typically less accurate than thatindicated by equation (11), has the advantage of not requiring a seriesof weights with arbitrary definitions. In yet another embodiment, thecollection of RoR(n) values are fit with a numerical function, such as alinear or non-linear function, and the effective RoR value can beestimated from the coefficients derived from the fit.

The dashed box 71 in FIG. 12A indicates a more complicated situationwhere profound motion is present in the ACC waveform; this, in turn,strongly influences the morphology of the RED/IR(PPG) waveforms, whilehaving little affect on the ECG waveform. FIG. 12D shows the resultingIR(PPG) waveform, along with markers 74-77 indicating various pointsalong the waveform where its foot and peak, in theory, could bedetermined. For example, application of the first and second firstderivative tests could indicate two successive pulses, with the pulsebetween markers 74, 75 and 76, 77 yielding sub-ratios indicated,respectively, by the dashed lines 79A and 79B. The actual pulse, whilestill affected by motion-related noise, lies roughly between the markers74, 75, and results in the sub-ratios calculated according to dashedlines 79A and shown in FIG. 12E. These sub-ratios are relatively noisycompared to those for the motion-free measurements indicated in FIG.12C, but if processed with a weighted average like that indicated inequation (11) would likely yield a SpO2 value with suitable accuracy. Incontrast, the ‘pulse’ between markers 76 and 77 is not caused by anactual heartbeat, and instead is an artifact resulting solely frommotion. Thus, sub-ratios calculated between these markers 76, 77, asindicated by dashed lines 79B, result in artificial data pointscharacterized by a large variation, as shown in FIG. 12F. The presenceof motion, as indicated by the ACC waveform, drives the algorithm toremove these data points from a SpO2 calculation, as they will notresult in an accurate value.

FIGS. 13A-D show an alternate embodiment of the invention that uses PTTto estimate if a pulse is corrupted by noise, and if so deploys a‘matched filter’ to process both the RED/IR(PPG) waveforms to maximizesignal-to-noise ratios of the pulses therein. With this embodiment thebody-worn monitor can determine accurate SpO2 and cNIBP values even whenmotion-related noise is present. To illustrate this approach, FIGS.13A-C feature a series of graphs on its left-hand side that show: i) howPTT is calculated from the time separating a peak of the ECG QRS andcorresponding foot of the IR(PPG) (FIG. 13A); 2) PTT measured from anunfiltered PPG waveform plotted as a function of heartbeat (13B); and 3)PTT measured from a PPG waveform processed with a matched filter, andplotted as a function of heartbeat (FIG. 13C). FIG. 13D, shown in theright-hand side of the figure, is a flowchart corresponding to thesegraphs that illustrates this two-part method for making measurementswhen motion-related noise is present. The flowchart, in particular,shows an alternative series of steps 105-111 for processing the ECG,RED/IR(PPG), and ACC waveforms that replace steps 37-41 in FIG. 7.

FIG. 13A shows PTT calculated from the time difference (ΔT) separatingthe ECG QRS and the foot of the IR(PPG) waveform. This waveform istypically used in place of the RED(PPG) because of its superiorsignal-to-noise ratio. As described above, the QRS features a sharp,easily measurable peak informally marking the beginning of the cardiaccycle. In this case the IR(PPG) waveform follows this feature by about220 ms; its foot is typically calculated by taking the second derivativeof the waveform after performing some initial filtering to removehigh-frequency noise, and then looking for a zero-point crossing.Referring to the flow chart shown in FIG. 13D, PTT is determined duringstep 105, and the process is repeated for consecutive heartbeats duringa measurement interval, which is typically 10-20 seconds (step 106).

Once PTT is determined for each heartbeat in the measurement interval, aseries of simple statistical filters are applied to detect pulses thatmay be corrupted by motion, and can thus potentially yield inaccuratevalues. In step 107, for example, a simple average (AVE) and standarddeviation (STDEV) are first calculated in a rolling manner, beginning atpulse N and extending out ε pulses, where ε is the number of pulseswithin the above-described measurement interval. These statisticalparameters can be updated for each subsequent pulse because of therolling calculation. If the PTT value for the immediate pulse is morethan 1 STDEV greater than the AVE for the preceding ε pulses, as shownin step 108, it is flagged as potentially originating from a IR(PPG)that is corrupted by motion. This simple filtering process is shownschematically by the window 112 in FIG. 13B (data points outside thiswindow 112 are flagged), and is indicated in the flow chart by step 107.A rapid change in PTT, however, may be a real occurrence resulting froman actual fluctuation in blood pressure. Thus, the algorithm processes acorresponding ACC waveform generated by the accelerometer within thewrist-worn transceiver to determine if motion was indeed present duringa time interval consistent with the PTT and its associated pulse (step109). During step 109 it may be determined that motion exceeded apre-determined coefficient (e.g. M_(max)). M_(max) can be determined,for example, from the power spectrum of time-dependent ACC waveformsthat are known to corrupt RED/IR(PPG) waveforms, such as those shown inFIGS. 14-17 and described below. In this case the pulse is considered tobe corrupted to an extent that neither PTT nor parameters associatedwith SpO2 (e.g. RED/IR(foot) and RED/IR(peak)) can be accuratelymeasured. The algorithm then returns to step 105 to resume calculatingPTT from the ECG and IR(PPG) waveforms.

On the other hand, if motion is determined to be less than M_(max) instep 109, then it is assumed that the PTT and corresponding pulse may berelatively uncorrupted, but are in need of additional filtering topotentially remove any noise that may have caused the abnormal PTTvalue. In this case, the pulse is filtered with a matched filter (alsoreferred to as a ‘North filter’ when used in telecommunications). Amatched filter is one which features an ideal frequency response thatmaximizes the signal-to-noise ratio for a given signal of a known shapein the time domain, particularly when the signal is subject to random,stochastic noise, such as that caused by motion. It involvesmathematically convolving the immediate pulse with a known good pulse,or ‘pulse template’, using a mathematical cross-correlation algorithm.The cross-correlation yields filtering parameters that, onceincorporated, represent a linear filter that in theory can optimize thesignal-to-noise ratio of the immediate pulse. Specifically, for thisapplication, a digital matched filter features an impulse responsecharacterized by coefficients h(k). This function represents thetime-reversed replica of the ideal signal to be detected, i.e. a pulsein the IR(PPG) measured during a time period where motion (as determinedfrom the ACC waveform) is not present. Alternatively, h(k) can bedetermined from a standard, pre-programmed pulse, determined fromwaveforms measured from a large group of patients, which represents aknown good pulse. In still other embodiments, this ‘textbook’pre-programmed pulse is used initially in the matched filter, and thenupdated as subsequent known good pulses are measured from the patient.The subsequent known good pulse can be just a section of a pulse (e.g.,near the foot or peak) that is known to be uncorrupted by noise. In anycase, assuming this pulse is represented by the pulse template functionx_(tp)(k), then the coefficients h(k) of the matched filter are given byequation (13):

h(k)=x _(tp)(N−k−1), where k=0, 1, . . . N−1  (13)

The digital matched filter can be represented as a finite impulseresponse filter with a typical transversal structure, with the outputy(i) of the filter shown in equation (14):

$\begin{matrix}{{y(i)} = {\int_{k = 0}^{k = {N - 1}}{{h(k)}{x(k)}{k}}}} & (14)\end{matrix}$

where x(k) are the samples of the immediate pulse (i.e. the input pulserequiring filtering), xp(k) are the samples of the pulse template, N isthe filter length, and i is a time shift index. From equations (13) and(14) it is evident that when the pulse template and the immediate pulseare identical, the output of the matched filter will be at its maximumvalue.

The matched filter improves the signal-to-noise ratio of the immediateinput pulse by an amount that is directly related to the length of thefilter (N). A filter length that is greater than or equal to theinterval between heart beats is required; preferably the filter lengthis greater than multiple heart beats.

Upon completion of step 110, PTT is calculated from the filteredwaveform, and the rolling AVE and STDEV statistics are recalculated(step 107). If the difference between the immediate PTT, as calculatedfrom the filtered waveform, is within +/−1 STDEV of the average as perstep 108, then the pulse is considered to be free of motion-relatedartifacts that may cause erroneous values of SpO2 and cNIBP. Such a caseis shown graphically in FIG. 13C. At this point, as indicated by step111, the ECG and RED/IR(PPG) are processed as per steps 42-46 and 48-50in FIG. 7, and SpO2 and cNIBP are determined.

A patient's activity level, as characterized by ACC waveforms, can havea significant impact on the RED/IR(PPG) and ECG waveforms used tomeasure both SpO2 and cNIBP. For example, FIGS. 14-17 showtime-dependent graphs of ECG, PPG, and ACC waveforms for a patient whois resting (FIG. 14), walking (FIG. 15), convulsing (FIG. 16), andfalling (FIG. 17). Each graph includes a single ECG waveform 80, 85, 90,95, PPG waveform 81, 86, 91, 96, and three ACC waveforms 82, 87, 92, 97.In all cases the PPG waveforms correspond to the IR(PPG) for the reasonsdescribed above. The ACC waveforms correspond to signals measured alongthe x, y, and z axes by a single accelerometer worn on the patient'swrist, similar to the accelerometer used to generate ACC waveforms inFIGS. 9B-11B.

The figures indicate that time-dependent properties of both ECG 80, 85,90, 95 and PPG 81, 86, 91, 96 waveforms can be strongly affected bycertain patient activities, which are indicated by the ACC waveforms 82,87, 92, 97. Accuracy of SpO2 and cNIBP calculated from these waveformsis therefore affected as well. FIG. 14, for example, shows datacollected from a patient at rest. This state is clearly indicated by theACC waveforms 82, which feature a relatively stable baseline along allthree axes of the accelerometer. High-frequency noise in all the ACCwaveforms 82, 87, 92, 97 shown in FIGS. 14-17 is due to electricalnoise, and is not indicative of patient motion in any way. The ECG 80and PPG 81 waveforms for this patient are correspondingly stable, thusallowing algorithms operating on the body-worn monitor to accuratelydetermine SpO2 (from the PPG waveform 81), along with heart rate andrespiratory rate (from the ECG waveform 80), cNIBP (from a PTT extractedfrom both the ECG 80 and PPG 81 waveforms). Based on the data shown inFIG. 14, algorithms operating on the body-worn monitor assume that vitalsigns calculated from a resting patient are relatively stable; thealgorithm therefore deploys normal threshold criteria for alarms/alerts,described below in Table 3, for patients in this state.

FIG. 15 shows ECG 85, PPG 86, and ACC 87 waveforms measured from awalking patient wearing the body-worn monitor. In this case, the ACCwaveform 87 clearly indicates a quasi-periodic modulation, with each‘bump’ in the modulation corresponding to a particular step. The ‘gaps’in the modulation, shown near 10, 19, 27, and 35 seconds, correspond toperiods when the patient stops walking and changes direction. Each bumpin the ACC waveform includes relatively high-frequency features (otherthan those associated with electrical noise, described above) thatcorrespond to walking-related movements of the patient's wrist.

The ECG waveform 85 measured from the walking patient is relativelyunaffected by motion, other than indicating an increase in heart rate(i.e., a shorter time separation between neighboring QRS complexes) andrespiratory rate (i.e. a higher frequency modulation of the waveform'senvelope) caused by the patient's exertion. The PPG waveform 86, incontrast, is strongly affected by this motion, and pulses within itbecome basically immeasurable. Its distortion is likely due in part to aquasi-periodic change in light levels, caused by the patient's swingingarm, and detected by the photodetector within the thumb-worn sensor.Movement of the patient's arm additionally affects blood flow in thethumb and can cause the optical sensor to move relative to the patient'sskin. The photodetector measures all of these artifacts, along with aconventional PPG signal (like the one shown in FIG. 14) caused byvolumetric expansion in the underlying arteries and capillaries withinthe patient's thumb. The artifacts produce radiation-inducedphotocurrent that is difficult to distinguish from normal PPG signalsused to calculate SpO2 and cNIBP. These vital signs are thus difficultor impossible to accurately measure when the patient is walking.

The body-worn monitor deploys multiple strategies to avoid generatingfalse alarms/alerts during a walking activity state. As described indetail below, the monitor can detect this state by processing the ACCwaveforms shown in FIG. 15 along with similar waveforms measured fromthe patient's bicep and chest. Walking typically elevates heart rate,respiratory rate, and blood pressure, and thus alarm thresholds forthese parameters, as indicated by Table 2, are systematically andtemporarily increased when this state is detected. Values above themodified thresholds are considered abnormal, and trigger an alarm. SpO2,unlike heart rate, respiratory rate and blood pressure, does nottypically increase with exertion. Thus the alarm thresholds for thisparameter, as shown in Table 2, do not change when the patient iswalking. Body temperature measured with the body-worn monitor typicallyincreases between 1-5%, depending on the physical condition of thepatient and the speed at which they are walking.

TABLE 2 motion-dependent alarm/alert thresholds and heuristic rules fora walking patient Modified Motion Threshold for Heuristic Rules forVital Sign State Alarms/Alerts Alarms/Alerts Blood Walking Increase(+10-30%) Ignore Threshold; Do Pressure Not Alarm/Alert (SYS, DIA) HeartRate Walking Increase Use Modified (+10-300%) Threshold; Alarm/Alert ifValue Exceeds Threshold Respiratory Walking Increase Ignore Threshold;Do Rate (+10-300%) Not Alarm/Alert SpO2 Walking No Change IgnoreThreshold; Do Not Alarm/Alert Temperature Walking Increase (+10-30%) UseOriginal Threshold; Alarm/Alert if Value Exceeds Threshold

To further reduce false alarms/alerts, software associated with thebody-worn monitor or remote monitor can deploy a series of ‘heuristicrules’ determined beforehand using practical, empirical studies. Theserules, for example, can indicate that a walking patient is likelyhealthy, breathing, and characterized by a normal SpO2. Accordingly, therules dictate that respiratory rate, blood pressure, and SpO2 valuesmeasured during a walking state that exceed predetermined alarm/alertthresholds are likely corrupted by artifacts; the system, in turn, doesnot sound the alarm/alert in this case. Heart rate, as indicated by FIG.15, and body temperature can typically be accurately measured even whena patient is walking; the heuristic rules therefore dictate the modifiedthresholds listed in Table 2 be used to generate alarms/alerts for theseparticular vital signs.

FIG. 16 shows ECG 90, PPG 91, and ACC 92 waveforms measured from apatient that is simulating convulsing by rapidly moving their arm backand forth. A patient undergoing a Gran-mal seizure, for example, wouldexhibit this type of motion. As is clear from the waveforms, the patientis at rest for the initial 10 seconds shown in the graph, during whichthe ECG 90 and PPG 91 waveforms are uncorrupted by motion. The patientthen begins a period of simulated, rapid convulsing that lasts for about12 seconds. A brief 5-second period of rest follows, and then convulsingbegins for another 12 seconds or so.

Convulsing modulates the ACC waveform 92 due to rapid motion of thepatient's arm, as measured by the wrist-worn accelerometer. Thismodulation is strongly coupled into the PPG waveform 91, likely becauseof the phenomena described above, i.e.: 1) ambient light coupling intothe oximetry probe's photodiode; 2) movement of the photodiode relativeto the patient's skin; and 3) disrupted blow flow underneath the probe.Note that from about 23-28 seconds the ACC waveform 92 is not modulated,indicating that the patient's arm is at rest. During this period theambient light is constant and the optical sensor is stationary relativeto the patient's skin. But the PPG waveform 91 is still stronglymodulated, albeit at a different frequency than the modulation thatoccurred when the patient's arm was moving, and the pulses therein aredifficult to resolve. This indicates that the disrupted blood flowunderneath the optical sensor continues even after the patient's armstops moving. Using this information, both ECG and PPG waveforms similarto those shown in FIG. 16 can be analyzed in conjunction with ACCwaveforms measured from groups of stationary and moving patients. Thesedata can then be analyzed to estimate the effects of specific motionsand activities on the ECG and PPG waveforms, and then deconvolute thesefactors using known mathematical techniques to effectively remove anymotion-related artifacts. The deconvoluted ECG and PPG waveforms canthen be used to calculate vital signs, as described in detail below.

The ECG waveform 90 is modulated by the patient's arm movement, but to alesser degree than the PPG waveform 91. In this case, modulation iscaused primarily by electrical ‘muscle noise’ instigated by theconvulsion and detected by the ECG electrodes, and well as byconvulsion-induced motion in the ECG cables and electrodes relative tothe patient's skin. Such motion is expected to have a similar affect ontemperature measurements, which are determined by a sensor that alsoincludes a cable.

Table 3, below, shows the modified threshold values and heuristic rulesfor alarms/alerts generated by a convulsing patient. In general, when apatient experiences convulsions, such as those simulated during the two12-second periods in FIG. 16, it is virtually impossible to accuratelymeasure any vital signs from the ECG 90 and PPG 91 waveforms. For thisreason the threshold values corresponding to each vital sign are notadjusted when convulsions are detected. Heart rate determined from theECG waveform, for example, is typically erroneously high due tohigh-frequency convulsions, and respiratory rate is immeasurable fromthe distorted waveform. Strong distortion of the optical waveform alsomakes both SpO2 and PPT-based cNIBP difficult or impossible to measure.For this reason, algorithms operating on either the body-worn monitor ora remote monitor will not generate alarms/alerts based on vital signswhen a patient is convulsing, as these vital signs will almost certainlybe corrupted by motion-related artifacts.

TABLE 3 motion-dependent alarm/alert thresholds and heuristic rules fora convulsing patient Modified Motion Threshold for Heuristic Rules forVital Sign State Alarms/Alerts Alarms/Alerts Blood Convulsing No ChangeIgnore Threshold; Pressure Generate Alarm/Alert (SYS, DIA) Because ofConvulsion Heart Rate Convulsing No Change Ignore Threshold; GenerateAlarm/Alert Because of Convulsion Respiratory Rate Convulsing No ChangeIgnore Threshold; Generate Alarm/Alert Because of Convulsion SpO2Convulsing No Change Ignore Threshold; Generate Alarm/Alert Because ofConvulsion Temperature Convulsing No Change Ignore Threshold; GenerateAlarm/Alert Because of Convulsion

Table 3 also shows the heuristic rules for convulsing patients. Here,the overriding rule is that a convulsing patient needs assistance, andthus an alarm/alert for this patient is generated regardless of theirvital signs (which, as described above, are likely inaccurate due tomotion-related artifacts). The system always generates an alarm/alertfor a convulsing patient.

FIG. 17 shows ECG 95, PPG 96, and ACC 97 waveforms measured from apatient that experiences a fall roughly 13 seconds into the measuringperiod. The ACC waveform 97 clearly indicates the fall with a sharpdecrease in its signal, followed by a short-term oscillatory signal, due(literally) to the patient bouncing on the floor. After the fall, ACCwaveforms 97 associated with the x, y, and z axes also show a prolongeddecrease in value due to the resulting change in the patient's posture.In this case, both the ECG 95 and PPG 96 waveforms are uncorrupted bymotion prior to the fall, but basically immeasurable during the fall,which typically takes only 1-2 seconds. Specifically, this activity addsvery high frequency noise to the ECG waveform 95, making it impossibleto extract heart rate and respiratory rate during this short timeperiod. Falling causes a sharp drop in the PPG waveform 96, presumablyfor the same reasons as described above (i.e. changes in ambient light,sensor movement, and disruption of blood flow) for walking andconvulsing, making it difficult to measure SpO2 and cNIBP.

After a fall, both the ECG 95 and PPG 96 waveforms are free fromartifacts, but both indicate an accelerated heart rate and relativelyhigh heart rate variability for roughly 10 seconds. During this periodthe PPG waveform 96 also shows distortion and a decrease in pulseamplitude. Without being bound to any theory, the increase in heart ratemay be due to the patient's baroreflex, which is the body's haemostaticmechanism for regulating and maintaining blood pressure. The baroreflex,for example, is initiated when a patient begins faint. In this case, thepatient's fall may cause a rapid drop in blood pressure, therebydepressing the baroreflex. The body responds by accelerating heart rate(indicated by the ECG waveform 95) and increasing blood pressure(indicated by a reduction in PTT, as measured from the ECG 95 and PPG 96waveforms) in order to deliver more blood to the patient's extremities.

Table 4 shows the heuristic rules and modified alarm thresholds for afalling patient. Falling, similar to convulsing, makes it difficult tomeasure waveforms and the vital signs calculated from them. Because ofthis and the short time duration associated with a fall, alarms/alertsbased on vital signs thresholds are not generated during an actualfalls. However, this activity, optionally coupled with prolongedstationary period or convulsion (both determined from the following ACCwaveform), generates an alarm/alert according to the heuristic rules.

TABLE 4 motion-dependent alarm/alert thresholds and heuristic rules fora falling patient Modified Motion Threshold for Heuristic Rules forVital Sign State Alarms/Alerts Alarms/Alerts Blood Pressure Falling NoChange Ignore Threshold; Generate (SYS, DIA) Alarm/Alert Because of FallHeart Rate Falling No Change Ignore Threshold; Generate Alarm/AlertBecause of Fall Respiratory Rate Falling No Change Ignore Threshold;Generate Alarm/Alert Because of Fall SpO2 Falling No Change IgnoreThreshold; Generate Alarm/Alert Because of Fall Temperature Falling NoChange Ignore Threshold; Generate Alarm/Alert Because of Fall

In addition to activity level, as described above and indicated in FIGS.14-17, a patient's posture can influence how the above-described systemgenerates alarms/alerts from SpO2, cNIBP, and other vital signs. Forexample, the alarms/alerts related to both SpO2 and cNIBP may varydepending on whether the patient is lying down or standing up. FIG. 18indicates how the body-worn monitor can determine motion-relatedparameters (e.g. degree of motion, posture, and activity level) from apatient 110 using time-dependent ACC waveforms continuously generatedfrom the three accelerometers 112, 113, 114 worn, respectively, on thepatient's chest, bicep, and wrist. As described above with reference toFIGS. 9A,B-11A,B, motion of the patient's hand is most likely to affectmeasurement of both RED/IR(PPG) waveforms, and this can best be detectedusing the accelerometer 114 affixed to the wrist. The height of thepatient's arm can affect the cNIBP measurement, as blood pressure canvary significantly due to hydrostatic forces induced by changes in armheight. Moreover, this phenomenon can be detected and exploited tocalibrate the cNIBP measurement, as described in detail in theabove-referenced patent application, the contents of which have beenpreviously incorporated by reference: BODY-WORN VITAL SIGN MONITOR WITHSYSTEM FOR DETECTING AND ANALYZING MOTION (U.S. Ser. No. 12/469,094;filed May 20, 2009). As described in this document, arm height can bedetermined using DC signals from the accelerometers 113, 114 disposed,respectively, on the patient's bicep and wrist. Posture, in contrast,can be exclusively determined by the accelerometer 112 worn on thepatient's chest. An algorithm operating on the wrist-worn transceiverextracts DC values from waveforms measured from this accelerometer andprocesses them with an algorithm described below to determine posture.

Specifically, posture is determined for a patient 110 using anglesdetermined between the measured gravitational vector and the axes of atorso coordinate space 111. The axes of this space 111 are defined in athree-dimensional Euclidean space where {right arrow over (R)}_(CV) isthe vertical axis, {right arrow over (R)}_(CH) is the horizontal axis,and {right arrow over (R)}_(CN) is the normal axis. These axes must beidentified relative to a ‘chest accelerometer coordinate space’ beforethe patient's posture can be determined.

The first step in determining a patient's posture is to identifyalignment of {right arrow over (R)}^(CV) in the chest accelerometercoordinate space. This can be determined in either of two approaches. Inthe first approach, {right arrow over (R)}_(CV) is assumed based on atypical alignment of the body-worn monitor relative to the patient.During a manufacturing process, these parameters are then preprogrammedinto firmware operating on the wrist-worn transceiver. In this procedureit is assumed that accelerometers within the body-worn monitor areapplied to each patient with essentially the same configuration. In thesecond approach, {right arrow over (R)}^(CV) is identified on apatient-specific basis. Here, an algorithm operating on the wrist-worntransceiver prompts the patient (using, e.g., video instructionoperating on the wrist-worn transceiver, or audio instructionstransmitted through a speaker) to assume a known position with respectto gravity (e.g., standing up with arms pointed straight down). Thealgorithm then calculates {right arrow over (R)}_(CV) from DC valuescorresponding to the x, y, and z axes of the chest accelerometer whilethe patient is in this position. This case, however, still requiresknowledge of which arm (left or right) the monitor is worn on, as thechest accelerometer coordinate space can be rotated by 180 degreesdepending on this orientation. A medical professional applying themonitor can enter this information using the GUI, described above. Thispotential for dual-arm attachment requires a set of two pre-determinedvertical and normal vectors which are interchangeable depending on themonitor's location. Instead of manually entering this information, thearm on which the monitor is worn can be easily determined followingattachment using measured values from the chest accelerometer values,with the assumption that {right arrow over (R)}_(CV) is not orthogonalto the gravity vector.

The second step in the procedure is to identify the alignment of {rightarrow over (R)}_(CN) in the chest accelerometer coordinate space. Themonitor can determine this vector, similar to how it determines {rightarrow over (R)}_(CV), with one of two approaches. In the first approachthe monitor assumes a typical alignment of the chest-worn accelerometeron the patient. In the second approach, the alignment is identified byprompting the patient to assume a known position with respect togravity. The monitor then calculates {right arrow over (R)}_(CN) fromthe DC values of the time-dependent ACC waveform.

The third step in the procedure is to identify the alignment of {rightarrow over (R)}_(CH) in the chest accelerometer coordinate space. Thisvector is typically determined from the vector cross product of {rightarrow over (R)}_(CV) and {right arrow over (R)}_(CN), or it can beassumed based on the typical alignment of the accelerometer on thepatient, as described above.

A patient's posture is determined using the coordinate system describedabove and in FIG. 18, along with a gravitational vector {right arrowover (R)}_(G) that extends normal from the patient's chest. The anglebetween {right arrow over (R)}_(CV) and {right arrow over (R)}_(G) isgiven by equation (14):

$\begin{matrix}{{\theta_{VG}\lbrack n\rbrack} = {\arccos\left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{CV}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{CV}}} \right)}} & (14)\end{matrix}$

where the dot product of the two vectors is defined as:

{right arrow over (R)} _(G) [n]·{right arrow over (R)} _(CV)=(y _(Cx)[n]×r _(CVx))+(y _(Cy) [n]×r _(CVy))+(y _(Cz) [n]×r _(CVz))  (15)

The definition of the norms of {right arrow over (R)}_(G) and {rightarrow over (R)}_(CV) are given by equations (16) and (17):

∥{right arrow over (R)} _(G) [n]∥=√{square root over ((y _(Cx) [n])²+(y_(Cy) [n])²+(y _(Cz) [n])²)}{square root over ((y _(Cx) [n])²+(y _(Cy)[n])²+(y _(Cz) [n])²)}{square root over ((y _(Cx) [n])²+(y _(Cy)[n])²+(y _(Cz) [n])²)}  (16)

∥{right arrow over (R)} _(CV)∥=√{square root over ((r _(CVx))²+(r_(CVy))²+(r _(CVz))²)}{square root over ((r _(CVx))²+(r _(CVy))²+(r_(CVz))²)}{square root over ((r _(CVx))²+(r _(CVy))²+(r _(CVz))²)}  (17)

As indicated in equation (18), the monitor compares the vertical angleθ_(VG) to a threshold angle to determine whether the patient is vertical(i.e. standing upright) or lying down:

if θ_(VG)≦45° then Torso State=0 the patient is upright  (18)

If the condition in equation (18) is met the patient is assumed to beupright, and their torso state, which is a numerical value equated tothe patient's posture, is equal to 0. The patient is assumed to be lyingdown if the condition in equation (18) is not met, i.e. θ_(VG)>45degrees. Their lying position is then determined from angles separatingthe two remaining vectors, as defined below.

The angle θ_(NG) between {right arrow over (R)}_(CN) and {right arrowover (R)}_(G) determines if the patient is lying in the supine position(chest up), prone position (chest down), or on their side. Based oneither an assumed orientation or a patient-specific calibrationprocedure, as described above, the alignment of {right arrow over(R)}_(CN) is given by equation (19), where i, j, k represent the unitvectors of the x, y, and z axes of the chest accelerometer coordinatespace respectively:

{right arrow over (R)} _(CN) r _(CNx) î+r _(CNy) j+r _(CNz) {circumflexover (k)}  (19)

The angle between {right arrow over (R)}_(CN) and {right arrow over(R)}_(G) determined from DC values extracted from the chestaccelerometer ACC waveform is given by equation (20):

$\begin{matrix}{{\theta_{NG}\lbrack n\rbrack} = {\arccos\left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{CN}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{CN}}} \right)}} & (20)\end{matrix}$

The body-worn monitor determines the normal angle θ_(NG) and thencompares it to a set of predetermined threshold angles to determinewhich position the patient is lying in, as shown in equation (21):

if θ_(NG)≦35° then Torso State=1, the patient is supine (21)

if θ_(NG)≧135° then Torso State=2, the patient is prone

If the conditions in equation (21) are not met then the patient isassumed to be lying on their side. Whether they are lying on their rightor left side is determined from the angle calculated between thehorizontal torso vector and measured gravitational vectors, as describedabove.

The alignment of {right arrow over (R)}_(CH) is determined using eitheran assumed orientation, or from the vector cross-product of {right arrowover (R)}_(CV) and {right arrow over (R)}_(CN) as given by equation(22), where i, j, k represent the unit vectors of the x, y, and z axesof the accelerometer coordinate space respectively. Note that theorientation of the calculated vector is dependent on the order of thevectors in the operation. The order below defines the horizontal axis aspositive towards the right side of the patient's body.

{right arrow over (R)}_(CH) r _(CVx) î+r _(CVy) ĵ+r _(CVz) {circumflexover (k)}={right arrow over (R)} _(CV) ×{right arrow over (R)}_(CN)  (22)

The angle θ_(HG) between {right arrow over (R)}_(CH) and {right arrowover (R)}_(G) is determined using equation (23):

$\begin{matrix}{{\theta_{HG}\lbrack n\rbrack} = {\arccos\left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{CH}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{CH}}} \right)}} & (23)\end{matrix}$

The monitor compares this angle to a set of predetermined thresholdangles to determine if the patient is lying on their right or left side,as given by equation (24):

if θ_(HG)≧90° then Torso State 3, the patient is on their right side(24)

if θ_(NG)<90° then Torso State 4, the patient is on their left side

Table 5 describes each of the above-described postures, along with acorresponding numerical torso state used to render, e.g., a particularicon:

TABLE 5 postures and their corresponding torso states Posture TorsoState Upright 0 supine: lying on back 1 prone: lying on chest 2 lying onright side 3 lying on left side 4 undetermined posture 5

FIGS. 19A and 19B show, respectively, graphs of time-dependent ACCwaveforms 100 measured along the x, y, and z-axes, and the torso states(i.e. postures) 101 determined from these waveforms for a movingpatient, as described above. As the patient moves, the DC values of theACC waveforms measured by the chest accelerometer vary accordingly, asshown by the graph 100 in FIG. 19A. The body-worn monitor processesthese values as described above to continually determine {right arrowover (R)}_(G) and the various quantized torso states for the patient, asshown in the graph 101 in FIG. 19B. The torso states yield the patient'sposture as defined in Table 5. For this study the patient rapidlyalternated between standing, lying on their back, chest, right side, andleft side within a time period of about 160 seconds. As described above,different alarm/alert conditions (e.g. threshold values) for vital signscan be assigned to each of these postures, or the specific postureitself may result in an alarm/alert. Additionally, the time-dependentproperties of the graph 101 can be analyzed (e.g. changes in the torsostates can be counted) to determine, for example, how often the patientmoves in their hospital bed. This number can then be equated to variousmetrics, such as a ‘bed sore index’ indicating a patient that is sostationary in their bed that lesions may result. Such a state could thenbe used to trigger an alarm/alert to the supervising medicalprofessional.

FIG. 20 is a schematic drawing showing time-dependent current pulses120, 121 used to drive the red and infrared LEDs in the pulse oximeterprobe. During a measurement of SpO2, both the red and infrared LEDs arealternately driven with separate current pulses 120, 121 having amagnitude ranging between about 10 and 40 mA, which can be controlleddynamically with a closed-loop system to maximize the RED/IR(PPG) signalstrength without saturating it, as described in more detail with regardto FIG. 22. To minimize power consumption, current pulses 120, 121typically drive the LEDs at 500 Hz for a time period of 100 μs, yieldinga duty cycle of 5%. The separation between neighboring current pulsesfor the LEDs is typically maximized according to the drive frequency,and as indicated by the dashed line 122 is 1 ms for a drive frequency of500 Hz.

FIG. 21 shows a circuit 175 that generates the current pulses 120, 121described above for powering the LEDs. The circuit 175 features anoperational amplifier 180 that receives a control voltage (V_(control))on its gating pin. The amplifier 180 is connected to a transistor 182and resistor 181 that, along with a supply voltage of 3.3V (typicallyfrom a Li:ion battery), generate the current pulses 120, 121 used todrive a dual red/infrared LED 150 operating at two wavelengths (660/905nm). The wavelength of the LED depends on the direction that it isbiased. To select the biasing direction, the circuit 175 features redcontrol lines 185, 190 and infrared control lines 187, 189 that connectdirectly to I/O lines in a microprocessor within the wrist-worntransceiver. During a measurement, the current pulses 120, 121 flow fromthe 3.3V supply voltage, across one direction of the LED 150, andultimately through the transistor 182 and resistor 181 to ground 183.The LED 150 is biased in a forward direction when control lines 185, 190are toggled closed, thereby supplying a drive current pulse ofi_(LED)=V_(control)/R₁ to the LED 150 to generate red radiation. Voltageflowing across the LED 150 is also decreased because it is a diode. Inthis case the control lines 187, 189 for infrared radiation are leftopen. As shown in FIG. 20, this configuration persists for 100 μs, afterwhich the red control lines 185, 190 are switched closed, and theinfrared control lines 187, 189 are switched open. This biases the LED150 in a backwards direction to generate infrared radiation according tothe above-described drive current. The alternating process is repeatedat 500 Hz. In both cases, red and infrared radiation that transmitsthrough the patient's thumb is detected by a photodetector 155 thatfeatures both an anode and cathode. Each black dot in FIG. 20 (five intotal) indicates a separate wire in the cable that connects the oximeterprobe to the wrist-worn transceiver. The wire associated with thecathode of the photodiode 155 also functions as a shield for theremaining 4 wires.

As shown in FIGS. 21 and 22, a thumb-worn pulse oximeter probe 294contains the red/infrared LED 150 along with a gain resistor 149indicating the specific wavelengths of both the red and infraredradiation. During a measurement, the microprocessor in the wrist-worntransceiver determines the value of the resistor 149 by monitoring avoltage drop across it; this value, in turn, is compared to a valuestored in memory to select the appropriate coefficients relating RoR toSpO2. The probe 294 generates alternating red and infrared radiationaccording to the timing diagram in FIG. 20 that passes through the baseof the patient's thumb 151, where it is partially absorbed by underlyingvasculature according to the patient's heart rate and SpO2 values.Radiation that transmits through the thumb 151 illuminates a photodiode155 that, in response, generates a photocurrent varying in magnitudewith the degree of optical absorption in the patient's thumb. Anamplifier circuit 140 beginning with a transimpedance amplifier 156receives the photocurrent and converts it to a corresponding voltagewhich is then amplified and filtered to generate the RED/IR(PPG)waveforms used to determine SpO2 and cNIBP.

The amplifier circuit 140 features separate channels for amplifying andfiltering signals corresponding to red radiation, infrared radiation,and ambient light detected by the photodiode 155 when the LED is notbiased to generate radiation. This occurs, for example, during the timeperiods shown in FIG. 20 when neither the red or infrared LED is driven.Once detected, the degree of ambient light can be subtracted from boththe red and infrared signals to improve their resultant signal-to-noiseratio. The amplifier channel corresponding to red radiation is activatedby a sample-and-hold integrated circuit 157 that is controlled by thesame control lines 185, 190 that drive the red LED, as shown in FIG. 20.When the red LED is driven, the sample-and-hold circuit 157 is switchedon, while similar components 164, 172 corresponding to the infraredsignals and ambient light are switched off. The sample-and-hold circuit157 samples and maintains an analog voltage from the transimpedanceamplifier 156, which then passes through a low-pass filter 158characterized by a 20 Hz cutoff. This filter removes any high-frequencynoise (e.g. 60 Hz electrical noise) that is not related to the RED(PPG),and yields a preliminary waveform that is digitized with ananalog-to-digital converter 176, and processed as described above togenerate a RED(DC) value. The preliminary waveform then passes through ahigh-pass filter 160 with a cutoff of 0.1 Hz to remove the DC portionand leave only the AC portion, which typically represents about 0.5-1%of the total signal magnitude. The AC portion is further amplified witha standard instrumentation amplifier 162 featuring a programmable gainthat is controlled with a 1.65 reference voltage and a digitalpotentiometer (not shown in the figure; this component may be includeddirectly in the instrumentation amplifier) featuring a variableresistance controlled by the microprocessor. The microprocessor selectsthe resistance (according to a predetermined binary command) andcorresponding gain to maximize the dynamic range of theanalog-to-digital converter 176. This process results in an amplifiedversion of the RED(AC) signal, which is then digitized with theanalog-to-digital converter 176 and then processed as described above.

The above-described filtering and amplification processes are repeatedwhen the infrared LED and a sample-and-hold integrated circuit 164corresponding to the infrared channel are activated with infrared I/Ocontrol lines 187, 189. The low-pass 166 and high-pass 168 filterscorresponding to this channel are identical to those used for the redchannel. The instrumentation amplifier 170 is also identical, but iscontrolled by a separate digital potentiometer to have a unique,uncoupled gain. This is because the IR(PPG) typically has a relativelylarge amplitude, and thus requires less amplification, than theRED(PPG). The channel corresponding to ambient light only requiresprocessing of DC signals, and thus includes a sample-and-hold integratedcircuit 172 that passes an analog voltage to a low-pass filter 174featuring a 20 Hz cutoff. The filtered value corresponding to ambientlight is then digitized with the analog-to-digital converter and thenprocessed as described above.

FIGS. 23A and 23B show how the body-worn monitor 190 described aboveattaches to a patient 270. These figures show two configurations of thesystem: FIG. 23A shows the system used during the indexing portion ofthe Composite Technique, and includes a pneumatic, cuff-based system285, while FIG. 23B shows the system used for subsequent SpO2 and cNIBPmeasurements. The indexing measurement typically takes about 60 seconds,and is typically performed once every 4-8 hours. Once the indexingmeasurement is complete the cuff-based system 285 is typically removedfrom the patient. The remainder of the time the system 190 performs theSpO2 and cNIBP measurements.

The body-worn monitor 190 features a wrist-worn transceiver 272,described in more detail in FIG. 24, featuring a touch panel interface273 that displays SpO2, blood pressure values and other vital signs. Awrist strap 290 affixes the transceiver 272 to the patient's wrist likea conventional wristwatch. A flexible cable 292 connects the transceiver272 to a pulse oximeter probe 294 that wraps around the base of thepatient's thumb. During a measurement, the probe 294 generates atime-dependent PPG waveform which is processed along with an ECG tomeasure cNIBP and SpO2. This provides an accurate representation ofblood pressure in the central regions of the patient's body, asdescribed above.

To determine ACC waveforms the body-worn monitor 190 features threeseparate accelerometers located at different portions on the patient'sarm and chest. The first accelerometer is surface-mounted on a circuitboard in the wrist-worn transceiver 272 and measures signals associatedwith movement of the patient's wrist. As described above, this motioncan also be indicative of that originating from the patient's fingers,which will affect the SpO2 measurement. The second accelerometer isincluded in a small bulkhead portion 296 included along the span of thecable 282. During a measurement, a small piece of disposable tape,similar in size to a conventional bandaid, affixes the bulkhead portion296 to the patient's arm. In this way the bulkhead portion 296 servestwo purposes: 1) it measures a time-dependent ACC waveform from themid-portion of the patient's arm, thereby allowing their posture and armheight to be determined as described in detail above; and 2) it securesthe cable 286 to the patient's arm to increase comfort and performanceof the body-worn monitor 190, particularly when the patient isambulatory.

The cuff-based module 285 features a pneumatic system 276 that includesa pump, valve, pressure fittings, pressure sensor, analog-to-digitalconverter, microcontroller, and rechargeable Li:ion battery. During anindexing measurement, the pneumatic system 276 inflates a disposablecuff 284 and performs two measurements according to the CompositeTechnique: 1) it performs an inflation-based measurement of oscillometryto determine values for SYS, DIA, and MAP; and 2) it determines apatient-specific relationship between PTT and MAP. These measurementsare described in detail in the above-referenced patent applicationentitled: ‘VITAL SIGN MONITOR FOR MEASURING BLOOD PRESSURE USINGOPTICAL, ELECTRICAL, AND PRESSURE WAVEFORMS’ (U.S. Ser. No. 12/138,194;filed Jun. 12, 2008), the contents of which have been previouslyincorporated herein by reference.

The cuff 284 within the cuff-based pneumatic system 285 is typicallydisposable and features an internal, airtight bladder that wraps aroundthe patient's bicep to deliver a uniform pressure field. During theindexing measurement, pressure values are digitized by the internalanalog-to-digital converter, and sent through a cable 286 according to aCAN protocol, along with SYS, DIA, and MAP blood pressures, to thewrist-worn transceiver 272 for processing as described above. Once thecuff-based measurement is complete, the cuff-based module 285 is removedfrom the patient's arm and the cable 282 is disconnected from thewrist-worn transceiver 272. cNIBP is then determined using PTT, asdescribed in detail above.

To determine an ECG, the body-worn monitor 190 features a small-scale,three-lead ECG circuit integrated directly into a bulkhead 274 thatterminates an ECG cable 282. The ECG circuit features an integratedcircuit that collects electrical signals from three chest-worn ECGelectrodes 278 a-c connected through cables 280 a-c. The ECG electrodes278 a-c are typically disposed in a conventional ‘Einthoven's Triangle’configuration which is a triangle-like orientation of the electrodes 278a-c on the patient's chest that features three unique ECG vectors. Fromthese electrical signals the ECG circuit determines up to three ECGwaveforms, which are digitized using an analog-to-digital convertermounted proximal to the ECG circuit, and sent through a cable 282 to thewrist-worn transceiver 272 according to the CAN protocol. There, the ECGand PPG waveforms are processed to determine the patient's bloodpressure. Heart rate and respiratory rate are determined directly fromthe ECG waveform using known algorithms, such as those described above.The cable bulkhead 274 also includes an accelerometer that measuresmotion associated with the patient's chest as described above.

There are several advantages of digitizing ECG and ACC waveforms priorto transmitting them through the cable 282. First, a single transmissionline in the cable 282 can transmit multiple digital waveforms, eachgenerated by different sensors. This includes multiple ECG waveforms(corresponding, e.g., to vectors associated with three, five, andtwelve-lead ECG systems) from the ECG circuit mounted in the bulkhead274, along with waveforms associated with the x, y, and z axes ofaccelerometers mounted in the bulkheads 274, 296. Limiting thetransmission line to a single cable reduces the number of wires attachedto the patient, thereby decreasing the weight and cable-related clutterof the body-worn monitor. Second, cable motion induced by an ambulatorypatient can change the electrical properties (e.g. electrical impedance)of its internal wires. This, in turn, can add noise to an analog signaland ultimately the vital sign calculated from it. A digital signal, incontrast, is relatively immune to such motion-induced artifacts.

More sophisticated ECG circuits can plug into the wrist-worn transceiverto replace the three-lead system shown in FIGS. 23A and 23B. These ECGcircuits can include, e.g., five and twelve leads.

FIG. 24 shows a close-up view of the wrist-worn transceiver 272. Asdescribed above, it attaches to the patient's wrist using a flexiblestrap 290 which threads through two D-ring openings in a plastic housing206. The transceiver 272 houses portions of the circuits 175, 140described in FIGS. 20 and 21, and additionally features a touchpaneldisplay 200 that renders a GUI 273 which is altered depending on theviewer (typically the patient or a medical professional). Specifically,the transceiver 272 includes a small-scale infrared barcode scanner 202that, during use, can scan a barcode worn on a badge of a medicalprofessional. The barcode indicates to the transceiver's software that,for example, a nurse or doctor is viewing the user interface. Inresponse, the GUI 273 displays vital sign data and other medicaldiagnostic information appropriate for medical professionals. Using thisGUI 273, the nurse or doctor, for example, can view the vital signinformation, set alarm parameters, and enter information about thepatient (e.g. their demographic information, medication, or medicalcondition). The nurse can press a button on the GUI 273 indicating thatthese operations are complete. At this point, the display 200 renders aninterface that is more appropriate to the patient, such as time of dayand battery power.

As described above, the transceiver 272 features three CAN connectors204 a-c on the side of its upper portion, each which supports the CANprotocol and wiring schematics, and relays digitized data to theinternal CPU. Digital signals that pass through the CAN connectorsinclude a header that indicates the specific signal (e.g. ECG, ACC, orpressure waveform from the cuff-based module) and the sensor from whichthe signal originated. This allows the CPU to easily interpret signalsthat arrive through the CAN connectors 204 a-c, and means that theseconnectors are not associated with a specific cable. Any cableconnecting to the transceiver can be plugged into any connector 204 a-c.As shown in FIG. 23A, the first connector 204 a receives the cable 282that transports a digitized ECG waveform determined from the ECG circuitand electrodes, and digitized ACC waveforms measured by accelerometersin the cable bulkhead 274 and the bulkhead portion 296 associated withthe ECG cable 282.

The second CAN connector 204 b shown in FIG. 22 receives the cable 286that connects to the pneumatic cuff-based system 285 used for thepressure-dependent indexing measurement (shown in FIG. 23A). Thisconnector 204 b receives a time-dependent pressure waveform delivered bythe pneumatic system 285 to the patient's arm, along with values forSYS, DIA, and MAP values determined during the indexing measurement. Thecable 286 unplugs from the connector 204 b once the indexing measurementis complete, and is plugged back in after approximately four hours foranother indexing measurement.

The final CAN connector 204 c can be used for an ancillary device, e.g.a glucometer, infusion pump, body-worn insulin pump, ventilator, orend-tidal CO₂ delivery system. As described above, digital informationgenerated by these systems will include a header that indicates theirorigin so that the CPU can process them accordingly.

The transceiver 272 includes a speaker 201 that allows a medicalprofessional to communicate with the patient using a voice over Internetprotocol (VOIP). For example, using the speaker 201 the medicalprofessional could query the patient from a central nursing station ormobile phone connected to a wireless, Internet-based network within thehospital. Or the medical professional could wear a separate transceiversimilar to the shown in FIG. 24, and use this as a communication device.In this application, the transceiver 272 worn by the patient functionsmuch like a conventional cellular telephone or ‘walkie talkie’: it canbe used for voice communications with the medical professional and canadditionally relay information describing the patient's vital signs andmotion. The speaker can also enunciate pre-programmed messages to thepatient, such as those used to calibrate the chest-worn accelerometersfor a posture calculation, as described above.

In addition to those methods described above, the body-worn monitor canuse a number of additional methods to calculate blood pressure and otherproperties from the optical and electrical waveforms. These aredescribed in the following co-pending patent applications, the contentsof which are incorporated herein by reference: 1) CUFFLESSBLOOD-PRESSURE MONITOR AND ACCOMPANYING WIRELESS, INTERNET-BASED SYSTEM(U.S. Ser. No. 10/709,015; filed Apr. 7, 2004); 2) CUFFLESS SYSTEM FORMEASURING BLOOD PRESSURE (U.S. Ser. No. 10/709,014; filed Apr. 7, 2004);3) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYING WEB SERVICESINTERFACE (U.S. Ser. No. 10/810,237; filed Mar. 26, 2004); 4) VITAL SIGNMONITOR FOR ATHLETIC APPLICATIONS (U.S. Ser. No.; filed Sep. 13, 2004);5) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYING WIRELESS MOBILEDEVICE (U.S. Ser. No. 10/967,511; filed Oct. 18, 2004); 6) BLOODPRESSURE MONITORING DEVICE FEATURING A CALIBRATION-BASED ANALYSIS (U.S.Ser. No. 10/967,610; filed Oct. 18, 2004); 7) PERSONAL COMPUTER-BASEDVITAL SIGN MONITOR (U.S. Ser. No. 10/906,342; filed Feb. 15, 2005); 8)PATCH SENSOR FOR MEASURING BLOOD PRESSURE WITHOUT A CUFF (U.S. Ser. No.10/906,315; filed Feb. 14, 2005); 9) PATCH SENSOR FOR MEASURING VITALSIGNS (U.S. Ser. No. 11/160,957; filed Jul. 18, 2005); 10) WIRELESS,INTERNET-BASED SYSTEM FOR MEASURING VITAL SIGNS FROM A PLURALITY OFPATIENTS IN A HOSPITAL OR MEDICAL CLINIC (U.S. Ser. No. 11/162,719;filed Sep. 9, 2005); 11) HAND-HELD MONITOR FOR MEASURING VITAL SIGNS(U.S. Ser. No. 11/162,742; filed Sep. 21, 2005); 12) CHEST STRAP FORMEASURING VITAL SIGNS (U.S. Ser. No. 11/306,243; filed Dec. 20, 2005);13) SYSTEM FOR MEASURING VITAL SIGNS USING AN OPTICAL MODULE FEATURING AGREEN LIGHT SOURCE (U.S. Ser. No. 11/307,375; filed Feb. 3, 2006); 14)BILATERAL DEVICE, SYSTEM AND METHOD FOR MONITORING VITAL SIGNS (U.S.Ser. No. 11/420,281; filed May 25, 2006); 15) SYSTEM FOR MEASURING VITALSIGNS USING BILATERAL PULSE TRANSIT TIME (U.S. Ser. No. 11/420,652;filed May 26, 2006); 16) BLOOD PRESSURE MONITOR (U.S. Ser. No.11/530,076; filed Sep. 8, 2006); 17) TWO-PART PATCH SENSOR FORMONITORING VITAL SIGNS (U.S. Ser. No. 11/558,538; filed Nov. 10, 2006);and, 18) MONITOR FOR MEASURING VITAL SIGNS AND RENDERING VIDEO IMAGES(U.S. Ser. No. 11/682,177; filed Mar. 5, 2007).

Other embodiments are also within the scope of the invention. Forexample, other measurement techniques, such as conventional oscillometrymeasured during deflation, can be used to determine SYS for theabove-described algorithms. Additionally, processing units and probesfor measuring pulse oximetry similar to those described above can bemodified and worn on other portions of the patient's body. For example,pulse oximetry probes with finger-ring configurations can be worn onfingers other than the thumb. Or they can be modified to attach to otherconventional sites for measuring SpO2, such as the ear, forehead, andbridge of the nose. In these embodiments the processing unit can be wornin places other than the wrist, such as around the neck (and supported,e.g., by a lanyard) or on the patient's waist (supported, e.g., by aclip that attaches to the patient's belt). In still other embodimentsthe probe and processing unit are integrated into a single unit.

In other embodiments, a set of body-worn monitors can continuouslymonitor a group of patients, wherein each patient in the group wears abody-worn monitor similar to those described herein. Additionally, eachbody-worn monitor can be augmented with a location sensor. The locationsensor includes a wireless component and a location-processing componentthat receives a signal from the wireless component and processes it todetermine a physical location of the patient. A processing component(similar to that described above) determines from the time-dependentwaveforms at least one vital sign, one motion parameter, and an alarmparameter calculated from the combination of this information. Awireless transceiver transmits the vital sign, motion parameter,location of the patient, and alarm parameter through a wireless system.A remote computer system featuring a display and an interface to thewireless system receives the information and displays it on a userinterface for each patient in the group.

In embodiments, the interface rendered on the display at the centralnursing station features a field that displays a map corresponding to anarea with multiple sections. Each section corresponds to the location ofthe patient and includes, e.g., the patient's vital signs, motionparameter, and alarm parameter. For example, the field can display a mapcorresponding to an area of a hospital (e.g. a hospital bay or emergencyroom), with each section corresponding to a specific bed, chair, orgeneral location in the area. Typically the display renders graphicalicons corresponding to the motion and alarm parameters for each patientin the group. In other embodiments, the body-worn monitor includes agraphical display that renders these parameters directly on the patient.

Typically the location sensor and the wireless transceiver operate on acommon wireless system, e.g. a wireless system based on 802.11,802.15.4, or cellular protocols. In this case a location is determinedby processing the wireless signal with one or more algorithms known inthe art. These include, for example, triangulating signals received fromat least three different base stations, or simply estimating a locationbased on signal strength and proximity to a particular base station. Instill other embodiments the location sensor includes a conventionalglobal positioning system (GPS).

The body-worn monitor can include a first voice interface, and theremote computer can include a second voice interface that integrateswith the first voice interface. The location sensor, wirelesstransceiver, and first and second voice interfaces can all operate on acommon wireless system, such as one of the above-described systems basedon 802.11 or cellular protocols. The remote computer, for example, canbe a monitor that is essentially identical to the monitor worn by thepatient, and can be carried or worn by a medical professional. In thiscase the monitor associated with the medical professional features a GUIwherein the user can select to display information (e.g. vital signs,location, and alarms) corresponding to a particular patient. Thismonitor can also include a voice interface so the medical professionalcan communicate directly with the patient.

Still other embodiments are within the scope of the following claims.

1. A method for monitoring a patient, comprising: (a) measuring a first time-dependent signal by detecting radiation emitted by a first radiation source after it passes through a portion of the patient; (b) measuring a second time-dependent signal by detecting radiation emitted by a second radiation source after it passes through a portion of the patient; (c) measuring a time-dependent motion signal with at least one motion sensor; (d) processing the time-dependent motion signal to determine at least one of the patient's posture and activity state; (e) processing both the first and second time-dependent signals to determine an oxygen saturation value for the patient; and, (f) suppressing an alarm based on the oxygen saturation value due to at least one of the patient's posture and activity state.
 2. A method for determining an oxygen saturation value for a patient, comprising: (a) measuring at least one optical signal with a first sensor; (b) measuring a motion signal with a motion sensor; (c) processing the optical signal to determine an oxygen saturation value; (d) processing the motion signal to determine at least of one of the patient's posture and activity state; and (e) suppressing an alarm based on the oxygen saturation value due to at least one of the patient's posture and activity state.
 3. The method of claim 1, wherein step (e) further comprises processing one of the patient's posture and activity state in combination with a parameter resulting from the comparison of the oxygen saturation value to the predetermined alarm criteria to suppress the alarm.
 4. The method of claim 3, wherein step (e) further comprises suppressing the alarm if the patient's posture is standing upright.
 5. The method of claim 3, wherein step (e) further comprises suppressing the alarm if the patient's posture changes from lying down to one of sitting and standing upright.
 6. The method of claim 3, wherein step (e) further comprises suppressing the alarm if the patient's posture changes from one of standing upright and sitting to lying down.
 7. The method of claim 1, wherein step (d) further comprises analyzing three time-dependent motion signals, each corresponding to a unique axis, to determine at least one of the patient's posture and activity state.
 8. The method of claim 7, wherein step (d) further comprises determining a vector from components of the three time-dependent motion signals.
 9. The method of claim 7, wherein step (d) further comprises comparing the vector to a coordinate space defined by three positional vectors, each indicating an orientation of the motion-detecting sensor on the patient.
 10. The method of claim 9, wherein a first positional vector corresponds to a vertical axis in the coordinate space, a second positional vector corresponds to a horizontal axis in the coordinate space, and the third positional vector corresponds to a normal axis extending normal to the patient's chest.
 11. The method of claim 10, wherein step (d) further comprises determining an angle associated with the vector.
 12. The method of claim 11, wherein step (d) further comprises comparing the vector to the coordinate space to determine the angle between the vector and at least one of the three positional vectors.
 13. The method of claim 11, wherein step (d) further comprises comparing the vector to the coordinate space to determine the angle between the vector and a vector corresponding to a vertical axis, or a property of the angle between the vector and the vector corresponding to the vertical axis.
 14. The method of claim 13, wherein step (d) further comprises comparing the angle to a threshold value that is substantially equivalent to 45 degrees, and wherein the patient's posture is estimated to be upright if the angle is less than the threshold value.
 15. The method of claim 13, wherein step (d) further comprises comparing the angle to a threshold value that is substantially equivalent to 45 degrees, and wherein the patient's posture is estimated to be lying down if the angle is greater than the threshold value.
 16. The method of claim 15, wherein step (d) further comprises comparing the vector to the coordinate space to determine the angle between the vector and a vector corresponding to a normal axis extending normal to the patient's chest, or a property of the angle between the vector and the vector corresponding to a normal axis extending normal to the patient's chest.
 17. The method of claim 16, wherein step (d) further comprises comparing the angle to a threshold value that is substantially equivalent to 35 degrees, and wherein the patient's posture is estimated to be supine if the angle is less than the threshold value.
 18. The method of claim 16, wherein step (d) further comprises comparing the angle to a threshold value that is substantially equivalent to 135 degrees, and wherein the patient's posture is estimated to be prone if the angle is greater than the threshold value.
 19. The method of claim 15, wherein step (d) further comprises comparing the vector to the coordinate space to determine the angle between the vector and a vector corresponding to a horizontal axis, or a property of the angle between the vector and the vector corresponding to the horizontal axis.
 20. The method of claim 19, wherein step (h) further comprises comparing the angle to a threshold value that is substantially equivalent to 90 degrees, and wherein the patient's posture is estimated to be lying on a first side if the angle is less than the threshold value.
 21. The method of claim 19, wherein step (h) further comprises comparing the angle to a threshold value that is substantially equivalent to 90 degrees, and wherein the patient's posture is estimated to be lying on a side opposite the first side if the angle is greater than the threshold value.
 22. The method of claim 1, wherein step (c) further comprises detecting a set of time-dependent motion signals with at least one motion-detecting sensor positioned on the patient's chest.
 23. The method of claim 1, where the at least one motion-detecting sensor is an accelerometer. 